Promoting fluid intake to increase urine volume for kidney stone prevention: Protocol for a randomized controlled efficacy trial of the sipIT intervention

BACKGROUND

Risk of kidney stone recurrence can be reduced by increasing fluid intake and urine production but most patients fail to adhere to recommended clinical guidelines. Patients have indicated that common barriers to fluid intake include a lack of thirst, forgetting to drink, and not having access to water. We developed the sipIT intervention to support patients' fluid intake with semi-automated tracking (via a mobile app, connected water bottle and a smartwatch clockface that detects drinking gestures) and provision of just-in-time text message reminders to drink when they do not meet the hourly fluid intake goal needed to achieve the recommended volume. This trial evaluates the efficacy of sipIT for increasing urine output in patients at risk for recurrence of kidney stones.

METHOD/DESIGN

Adults with a history of kidney stones and lab-verified low urine production (<2 L/day) will be randomly assigned to receive either usual care (education and encouragement to meet fluid intake guidelines) or usual care plus the sipIT intervention. The primary outcome is 24-h urine volume; secondary outcomes include urinary supersaturations, past week fluid intake, and experienced automaticity of fluid intake. Outcomes will be assessed at baseline, 1 month, 3 months, and 12 months.

CONCLUSIONS

The sipIT intervention is the first to prompt periodic fluid intake through integration of just-in-time notifications and semi-automated tracking. If sipIT is more efficacious than usual care, this intervention provides an innovative treatment option for patients needing support in meeting fluid intake guidelines for kidney stone prevention.

Digital Methods for Performing Daily Tasks Among Older Adults: An Initial Report of Frequency of Use and Perceived Utility

OBJECTIVE

Digital technologies permit new ways of performing instrumental activities of daily living (iADLs) for older adults, but these approaches are not usually considered in existing iADL measures. The current study investigated how a sample of older adults report using digital versus analog approaches for iADLs.

METHOD

248 older adults completed the Digital and Analog Daily Activities Survey, a newly developed measure of how an individual performs financial, navigation, medication, and other iADLs.

RESULTS

The majority of participants reported regularly using digital methods for some iADLs, such as paying bills (67.7%) and using GPS (67.7%). Low digital adopters were older than high adopters (F(2, 245) = 12.24, p < .001), but otherwise the groups did not differ in terms of gender, years of education, or history of neurological disorders. Participants who used digital methods relatively more than analog methods reported greater levels of satisfaction with their approach and fewer daily errors.

CONCLUSIONS

Many older adults have adopted digital technologies for supporting daily tasks, which suggests limitations to the validity of current iADL assessments. By capitalizing on existing habits and enriching environments with new technologies, there are opportunities to promote technological reserve in older adults in a manner that sustains daily functioning.

Auditory chaos classification in real-world environments

Background & motivation

Household chaos is an established risk factor for child development. However, current methods for measuring household chaos rely on parent surveys, meaning existing research efforts cannot disentangle potentially dynamic bidirectional relations between high chaos environments and child behavior problems.

Proposed approach

We train and make publicly available a classifier to provide objective, high-resolution predictions of household chaos from real-world child-worn audio recordings. To do so, we collect and annotate a novel dataset of ground-truth auditory chaos labels compiled from over 411 h of daylong recordings collected via audio recorders worn by N = 22 infants in their homes. We leverage an existing sound event classifier to identify candidate high chaos segments, increasing annotation efficiency 8.32× relative to random sampling.

Result

Our best-performing model successfully classifies four levels of real-world household auditory chaos with a macro F1 score of 0.701 (Precision: 0.705, Recall: 0.702) and a weighted F1 score of 0.679 (Precision: 0.685, Recall: 0.680).

Significance

In future work, high-resolution objective chaos predictions from our model can be leveraged for basic science and intervention, including testing theorized mechanisms by which chaos affects children's cognition and behavior. Additionally, to facilitate further model development we make publicly available the first and largest balanced annotated audio dataset of real-world household chaos.

Feasibility of Mini sipIT Behavioral Intervention to Increase Urine Volume in Patients With Kidney Stones

OBJECTIVE

To determine the feasibility and acceptability of mini sipIT, a context-sensitive reminder system that incorporates a connected water bottle and mobile app with text messaging, for kidney stone patients who have poor adherence to increasing fluid intake for prevention.

METHODS

Patients with a history of kidney stones and urine volume <2L/d participated in a 1-month single-group feasibility trial. Patients used a connected water bottle and received text message reminders when fluid intake goals weren't met. Perceptions of drinking behavior, intervention acceptability, and 24-hour urine volumes were obtained at baseline and 1-month.

RESULTS

Patients with a history of kidney stones were enrolled (n = 26, 77% female, age=50.4 ± 14.2years). Over 90% of patients used the bottle or app daily. Most patients perceived that mini sipIT intervention helped them to increase their fluid intake (85%) and reach their fluid intake goals (65%). There was a significant increase in average 24-hour urine volume after the 1-month intervention compared to baseline (2006.5 ± 980.8 mL vs 1352.7 ± 449.9 mL, t (25)= 3.66, P = .001, g= 0.78), with 73% of patients having higher 24-hour urine volumes at the end of the trial.

CONCLUSION

Mini sipIT behavioral intervention and outcome assessments are feasible for patients and may lead to significant increases in 24-hour urine volume. Digital tools in combination with behavioral science may improve adherence to fluid intake recommendations for kidney stone prevention, however, rigorous efficacy trials are necessary.

A Conceptual Model for Mobile Health-enabled Slow Eating Strategies

Ingestive behaviors (IBs) (eg, bites, chews, oral processing, swallows, pauses) have meaningful roles in enhancing satiety, promoting fullness, and decreasing food consumption, and thus may be an underused strategy for obesity prevention and treatment. Limited IB monitoring research has been conducted because of a lack of accurate automated measurement capabilities outside laboratory settings. Self-report methods are used, but they have questionable validity and reliability. This paper aimed to present a conceptual model in which IB, specifically slow eating, supported by technological advancements, contributes to controlling hedonic and homeostatic processes, providing an opportunity to reduce energy intake, and improve health outcomes.

Lifelong Adaptive Machine Learning for Sensor-Based Human Activity Recognition Using Prototypical Networks

Continual learning (CL), also known as lifelong learning, is an emerging research topic that has been attracting increasing interest in the field of machine learning. With human activity recognition (HAR) playing a key role in enabling numerous real-world applications, an essential step towards the long-term deployment of such systems is to extend the activity model to dynamically adapt to changes in people's everyday behavior. Current research in CL applied to the HAR domain is still under-explored with researchers exploring existing methods developed for computer vision in HAR. Moreover, analysis has so far focused on task-incremental or class-incremental learning paradigms where task boundaries are known. This impedes the applicability of such methods for real-world systems. To push this field forward, we build on recent advances in the area of continual learning and design a lifelong adaptive learning framework using Prototypical Networks, LAPNet-HAR, that processes sensor-based data streams in a task-free data-incremental fashion and mitigates catastrophic forgetting using experience replay and continual prototype adaptation. Online learning is further facilitated using contrastive loss to enforce inter-class separation. LAPNet-HAR is evaluated on five publicly available activity datasets in terms of its ability to acquire new information while preserving previous knowledge. Our extensive empirical results demonstrate the effectiveness of LAPNet-HAR in task-free CL and uncover useful insights for future challenges.

INFANT CRYING DETECTION IN REAL-WORLD ENVIRONMENTS

Most existing cry detection models have been tested with data collected in controlled settings. Thus, the extent to which they generalize to noisy and lived environments is unclear. In this paper, we evaluate several established machine learning approaches including a model leveraging both deep spectrum and acoustic features. This model was able to recognize crying events with F1 score 0.613 (Precision: 0.672, Recall: 0.552), showing improved external validity over existing methods at cry detection in everyday real-world settings. As part of our evaluation, we collect and annotate a novel dataset of infant crying compiled from over 780 hours of labeled real-world audio data, captured via recorders worn by infants in their homes, which we make publicly available. Our findings confirm that a cry detection model trained on in-lab data underperforms when presented with real-world data (in-lab test F1: 0.656, real-world test F1: 0.236), highlighting the value of our new dataset and model.

Feasibility of a Sensor-Controlled Digital Game for Heart Failure Self-management: Randomized Controlled Trial

BACKGROUND

Poor self-management of heart failure (HF) contributes to devastating health consequences. Our innovative sensor-controlled digital game (SCDG) integrates data from sensors to trigger game rewards, progress, and feedback based on the real-time behaviors of individuals with HF.

OBJECTIVE

The aim of this study is to compare daily weight monitoring and physical activity behavior adherence by older adults using an SCDG intervention versus a sensors-only intervention in a feasibility randomized controlled trial.

METHODS

English-speaking adults with HF aged 55 years or older who owned a smartphone and could walk unassisted were recruited from Texas and Oklahoma from November 2019 to August 2020. Both groups were given activity trackers and smart weighing scales to track behaviors for 12 weeks. The feasibility outcomes of recruitment, retention, intervention engagement, and satisfaction were assessed. In addition to daily weight monitoring and physical activity adherence, the participants' knowledge, functional status, quality of life, self-reported HF behaviors, motivation to engage in behaviors, and HF-related hospitalization were also compared between the groups at baseline and at 6, 12, and 24 weeks.

RESULTS

A total of 38 participants with HF-intervention group (IG; 19/38, 50%) and control group (CG; 19/38, 50%)-were enrolled in the study. Of the 38 participants, 18 (47%) were women, 18 (47%) were aged 65 years or older, 21 (55%) had been hospitalized with HF in the past 6 months, and 29 (76%) were White. Furthermore, of these 38 participants, 31 (82%)-IG (15/19, 79%) and CG (16/19, 84%)-had both weight monitoring and physical activity data at the end of 12 weeks, and 27 (71%)-IG (14/19, 74%) and CG (13/19, 68%)-participated in follow-up assessments at 24 weeks. For the IG participants who installed the SCDG app (15/19, 79%), the number of days each player opened the game app was strongly associated with the number of days the player engaged in weight monitoring (r=0.72; P=.04) and the number of days with physical activity step data (r=0.9; P<.001). The IG participants who completed the satisfaction survey (13/19, 68%) reported that the SCDG was easy to use. Trends of improvement in daily weight monitoring and physical activity in the IG, as well as within-group improvements in HF functional status, quality of life, knowledge, self-efficacy, and HF hospitalization in both groups, were observed in this feasibility trial.

CONCLUSIONS

Playing an SCDG on smartphones was feasible and acceptable for older adults with HF for motivating daily weight monitoring and physical activity. A larger efficacy trial of the SCDG intervention will be needed to validate trends of improvement in daily weight monitoring and physical activity behaviors.

TRIAL REGISTRATION

ClinicalTrials.gov NCT03947983; https://clinicaltrials.gov/ct2/show/NCT03947983.

Multi-modal data collection for measuring health, behavior, and living environment of large-scale participant cohorts

BACKGROUND

As mobile technologies become ever more sensor-rich, portable, and ubiquitous, data captured by smart devices are lending rich insights into users' daily lives with unprecedented comprehensiveness and ecological validity. A number of human-subject studies have been conducted to examine the use of mobile sensing to uncover individual behavioral patterns and health outcomes, yet minimal attention has been placed on measuring living environments together with other human-centered sensing data. Moreover, the participant sample size in most existing studies falls well below a few hundred, leaving questions open about the reliability of findings on the relations between mobile sensing signals and human outcomes.

RESULTS

To address these limitations, we developed a home environment sensor kit for continuous indoor air quality tracking and deployed it in conjunction with smartphones, Fitbits, and ecological momentary assessments in a cohort study of up to 1,584 college student participants per data type for 3 weeks. We propose a conceptual framework that systematically organizes human-centric data modalities by their temporal coverage and spatial freedom. Then we report our study procedure, technologies and methods deployed, and descriptive statistics of the collected data that reflect the participants' mood, sleep, behavior, and living environment.

CONCLUSIONS

We were able to collect from a large participant cohort satisfactorily complete multi-modal sensing and survey data in terms of both data continuity and participant adherence. Our novel data and conceptual development provide important guidance for data collection and hypothesis generation in future human-centered sensing studies.

Just-in-time adaptive intervention to promote fluid consumption in patients with kidney stones

OBJECTIVE

Kidney stones are painful and costly. Prevention guidelines emphasize a simple behavior change: increasing fluid intake and urine output. Unfortunately, adherence to those prevention guidelines is limited, and patients report forgetting or not being thirsty enough. This study evaluated the acceptability of using semiautomated tracking of fluid consumption to trigger just-in-time reminders to drink and increase the experienced automaticity of fluid intake.

METHOD

In a single-group trial, participants with a history of kidney stones (n = 31) used the sip^IT digital tools (H2OPal connected water bottle, H2OPal mobile app for self-tracking, Fitbit smartwatch app for gesture detection) for 3 months.

RESULTS

The semiautomated monitoring system detected 46,654 drinking events. From baseline to 1-month follow-up, the experienced automaticity of fluid intake increased significantly (d = 0.50) and remained elevated at 3-month follow-up (d = 0.64). A major barrier to adherence (lack of thirst) decreased from baseline to follow-ups. Retention rates and participant feedback indicated that this digital tool was acceptable to patients.

CONCLUSION

Semiautomated tracking of fluid consumption can be used to trigger just-in-time reminders. Based on this demonstration, the sip^IT tools are ready for testing in a rigorous Phase II trial to evaluate efficacy for increasing fluid consumption and urine output as recommended for preventing the recurrence of kidney stones. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

Usability Testing of a Sensor-Controlled Digital Game to Engage Older Adults with Heart Failure in Physical Activity and Weight Monitoring

BACKGROUND

Poor self-management of heart failure (HF) has contributed to poor health outcomes. Sensor-controlled digital games (SCDGs) integrates data from behavior-tracking sensors to trigger progress, rewards, content, and positive feedback in a digital game to motivate real-time behaviors.

OBJECTIVES

To assess the usability of an SCDG prototype over a week of game-playing among 10 older adults with HF in their homes.

METHODS

During initial play, participants' SCDG experiences were observed in their homes using a checklist based on the seven-item Serious Game User Evaluator (SeGUE) instrument. After a week of game-playing, participants completed a survey guided by the Intrinsic Motivation Inventory, to provide their perceptions of the SCDG's usability. Qualitative analysis via semistructured interview-derived themes on experiences playing the SCDG, perceptions regarding engaging with the SCDG, and any usability issues encountered.

RESULTS

Ten HF participants (50% women and 50% White) played the SCDG for an average of 6 out of 7 days. Nine found the SCDG to be interesting, satisfying, and easy to play. The average step count over a week was 4,117 steps (range: 967-9,892). Average adherence with weight monitoring was 5.9 days in a week. Qualitative analysis yielded outcomes regarding attitudes toward SCDG, and barriers and facilitators that influenced participants' engagement with the SCDG.

CONCLUSION

To the best of the authors' knowledge, this usability and feasibility study is the first to report an SCDG designed to improve HF self-management behaviors of older adults in their homes. Future research should consider several issues, such as user profiles, prior game-playing experiences, and network conditions most suitable for connected health interventions for older adults living in the community.

Eating Episode Detection with Jawbone-Mounted Inertial Sensing

Recent work in Automated Dietary Monitoring (ADM) has shown promising results in eating detection by tracking jawbone movements with a proximity sensor mounted on a necklace. A significant challenge with this approach, however, is that motion artifacts introduced by natural body movements cause the necklace to move freely and the sensor to become misaligned. In this paper, we propose a different but related approach: we developed a small wireless inertial sensing platform and perform eating detection by mounting the sensor directly on the underside of the jawbone. We implemented a data analysis pipeline to recognize eating episodes from the inertial sensor data, and evaluated our approach in two different conditions: in the laboratory and in naturalistic settings. We demonstrated that in the lab (n=9), the system can detect eating with 91.7% precision and 91.3% recall using the leave-one-participant-out cross-validation (LOPO-CV) performance metric. In naturalistic settings, we obtained an average precision of 92.3% and a recall of 89.0% (n=14). These results represent a significant improvement (>10% in F1 score) over state-of-the-art necklace-based approaches. Additionally, this work presents a wearable device that is more inconspicuous and thus more likely to be adopted in clinical applications.

Automatic, wearable-based, in-field eating detection approaches for public health research: a scoping review

Dietary intake, eating behaviors, and context are important in chronic disease development, yet our ability to accurately assess these in research settings can be limited by biased traditional self-reporting tools. Objective measurement tools, specifically, wearable sensors, present the opportunity to minimize the major limitations of self-reported eating measures by generating supplementary sensor data that can improve the validity of self-report data in naturalistic settings. This scoping review summarizes the current use of wearable devices/sensors that automatically detect eating-related activity in naturalistic research settings. Five databases were searched in December 2019, and 618 records were retrieved from the literature search. This scoping review included N = 40 studies (from 33 articles) that reported on one or more wearable sensors used to automatically detect eating activity in the field. The majority of studies (N = 26, 65%) used multi-sensor systems (incorporating > 1 wearable sensors), and accelerometers were the most commonly utilized sensor (N = 25, 62.5%). All studies (N = 40, 100.0%) used either self-report or objective ground-truth methods to validate the inferred eating activity detected by the sensor(s). The most frequently reported evaluation metrics were Accuracy (N = 12) and F1-score (N = 10). This scoping review highlights the current state of wearable sensors' ability to improve upon traditional eating assessment methods by passively detecting eating activity in naturalistic settings, over long periods of time, and with minimal user interaction. A key challenge in this field, wide variation in eating outcome measures and evaluation metrics, demonstrates the need for the development of a standardized form of comparability among sensors/multi-sensor systems and multidisciplinary collaboration.

Role of Digital Games in Self-Management of Cardiovascular Diseases: A Scoping Review

OBJECTIVE

Examine research on the use of digital games to improve self-management (SM) behaviors in patients diagnosed with cardiovascular diagnoses of hypertension, coronary artery disease, heart failure, or myocardial infarction.

MATERIALS AND METHODS

For this scoping review, the CINAHL, PubMed, and Web of Science databases were searched for studies published from January 1, 2008 to December 20, 2017 using terms relevant to digital games and cardiovascular diseases (CVDs).

RESULTS

Eight articles met the inclusion/exclusion criteria, seven of which presented studies with participants 50 years or older. Five of the eight studies assessed physical activity. Only two studies included a control group. Digital games significantly improved exercise capacity and energy expenditure but did not affect quality of life, self-efficacy, anxiety, or depression. Digital games were found enjoyable by 79%-93% of participants, including those with lower education or age; however, barriers to game use included being tired or bored, lack of interest in digital games, poor perception of fitness through games, sensor limitations, conflicts with daily life routine, and preferences for group exercise. Average adherence ranged from 70% to 100% over 2 weeks to 6 months of study duration, with higher adherence rates in studies that included human contact through supervision or social support.

CONCLUSION

Paucity of studies about digital games for CVD SM behaviors precludes the need to undertake a full systematic review. Future studies examining digital games should include larger sample sizes, longer durations, game-design guided by behavioral change theoretical frameworks, and CVD SM behaviors in addition to physical activity behaviors.

Towards a Generalizable Method for Detecting Fluid Intake with Wrist-Mounted Sensors and Adaptive Segmentation

Over the last decade, advances in mobile technologies have enabled the development of intelligent systems that attempt to recognize and model a variety of health-related human behaviors. While automated dietary monitoring based on passive sensors has been an area of increasing research activity for many years, much less attention has been given to tracking fluid intake. In this work, we apply an adaptive segmentation technique on a continuous stream of inertial data captured with a practical, off-the-shelf wrist-mounted device to detect fluid intake gestures passively. We evaluated our approach in a study with 30 participants where 561 drinking instances were recorded. Using a leave-one-participant-out (LOPO), we were able to detect drinking episodes with 90.3% precision and 91.0% recall, demonstrating the generalizability of our approach. In addition to our proposed method, we also contribute an anonymized and labeled dataset of drinking and non-drinking gestures to encourage further work in the field.

Exploring Symmetric and Asymmetric Bimanual Eating Detection with Inertial Sensors on the Wrist

Motivated by health applications, eating detection with off-the-shelf devices has been an active area of research. A common approach has been to recognize and model individual intake gestures with wrist-mounted inertial sensors. Despite promising results, this approach is limiting as it requires the sensing device to be worn on the hand performing the intake gesture, which cannot be guaranteed in practice. Through a study with 14 participants comparing eating detection performance when gestural data is recorded with a wrist-mounted device on (1) both hands, (2) only the dominant hand, and (3) only the non-dominant hand, we provide evidence that a larger set of arm and hand movement patterns beyond food intake gestures are predictive of eating activities when L1 or L2 normalization is applied to the data. Our results are supported by the theory of asymmetric bimanual action and contribute to the field of automated dietary monitoring. In particular, it shines light on a new direction for eating activity recognition with consumer wearables in realistic settings.

Hierarchical Span-Based Conditional Random Fields for Labeling and Segmenting Events in Wearable Sensor Data Streams

The field of mobile health (mHealth) has the potential to yield new insights into health and behavior through the analysis of continuously recorded data from wearable health and activity sensors. In this paper, we present a hierarchical span-based conditional random field model for the key problem of jointly detecting discrete events in such sensor data streams and segmenting these events into high-level activity sessions. Our model includes higher-order cardinality factors and inter-event duration factors to capture domain-specific structure in the label space. We show that our model supports exact MAP inference in quadratic time via dynamic programming, which we leverage to perform learning in the structured support vector machine framework. We apply the model to the problems of smoking and eating detection using four real data sets. Our results show statistically significant improvements in segmentation performance relative to a hierarchical pairwise CRF.

Predicting Daily Activities From Egocentric Images Using Deep Learning

We present a method to analyze images taken from a passive egocentric wearable camera along with the contextual information, such as time and day of week, to learn and predict everyday activities of an individual. We collected a dataset of 40,103 egocentric images over a 6 month period with 19 activity classes and demonstrate the benefit of state-of-the-art deep learning techniques for learning and predicting daily activities. Classification is conducted using a Convolutional Neural Network (CNN) with a classification method we introduce called a late fusion ensemble. This late fusion ensemble incorporates relevant contextual information and increases our classification accuracy. Our technique achieves an overall accuracy of 83.07% in predicting a person's activity across the 19 activity classes. We also demonstrate some promising results from two additional users by fine-tuning the classifier with one day of training data.

Barriers and Negative Nudges: Exploring Challenges in Food Journaling

Although food journaling is understood to be both important and difficult, little work has empirically documented the specific challenges people experience with food journals. We identify key challenges in a qualitative study combining a survey of 141 current and lapsed food journalers with analysis of 5,526 posts in community forums for three mobile food journals. Analyzing themes in this data, we find and discuss barriers to reliable food entry, negative nudges caused by current techniques, and challenges with social features. Our results motivate research exploring a wider range of approaches to food journal design and technology.

Inferring Meal Eating Activities in Real World Settings from Ambient Sounds: A Feasibility Study

Dietary self-monitoring has been shown to be an effective method for weight-loss, but it remains an onerous task despite recent advances in food journaling systems. Semi-automated food journaling can reduce the effort of logging, but often requires that eating activities be detected automatically. In this work we describe results from a feasibility study conducted in-the-wild where eating activities were inferred from ambient sounds captured with a wrist-mounted device; twenty participants wore the device during one day for an average of 5 hours while performing normal everyday activities. Our system was able to identify meal eating with an F-score of 79.8% in a person-dependent evaluation, and with 86.6% accuracy in a person-independent evaluation. Our approach is intended to be practical, leveraging off-the-shelf devices with audio sensing capabilities in contrast to systems for automated dietary assessment based on specialized sensors.

A Practical Approach for Recognizing Eating Moments with Wrist-Mounted Inertial Sensing

Recognizing when eating activities take place is one of the key challenges in automated food intake monitoring. Despite progress over the years, most proposed approaches have been largely impractical for everyday usage, requiring multiple on-body sensors or specialized devices such as neck collars for swallow detection. In this paper, we describe the implementation and evaluation of an approach for inferring eating moments based on 3-axis accelerometry collected with a popular off-the-shelf smartwatch. Trained with data collected in a semi-controlled laboratory setting with 20 subjects, our system recognized eating moments in two free-living condition studies (7 participants, 1 day; 1 participant, 31 days), with F-scores of 76.1% (66.7% Precision, 88.8% Recall), and 71.3% (65.2% Precision, 78.6% Recall). This work represents a contribution towards the implementation of a practical, automated system for everyday food intake monitoring, with applicability in areas ranging from health research and food journaling.