Determinants of Open Defecation Among Rural Women in Ghana: Analysis of Demographic and Health Surveys.

Open defecation continuously remains a major global sanitation challenge, contributing to an estimated 1.6 million deaths per year. Ghana ranks second in Africa for open defecation and had the fourth-lowest sanitation coverage in 2010. Evidence indicates that about 32% of the rural Ghanaian population still practice open defecation due to lack of access to basic sanitation facilities, drifting the country from achieving universal access to sanitation by 2030. Women, particularly those in rural areas, are disproportionately affected by open defecation, facing heightened health risks, harassment, and a loss of dignity. Even though previous studies on open defecation in Ghana exist, they lack national representation and neglect women in rural residents who are disproportionally affected by the repercussions of open defecation. Examining that rural women will contribute to heightening their own vulnerability to health risks by practising open defecation is essential to bridging the literature gap on open defecation practices among rural women. The study investigated determinants of open defecation among rural women in Ghana using data from the female files of the 2003, 2008 and 2014 Demographic and Health Surveys (DHS). A total of 4,284 rural women with complete information on variables of interest were included in the study. The outcome variable was 'open defecation', whilst 14 key explanatory variables (e.g., age, education, wealth status, among others) were used. Two logistic regression models were built, and the outputs were reported in odds ratio. Descriptively, 42 in every 100 women aged 15 to 49 practiced open defecation (n = 1811, 95% CI = 49-52). Open defecation (OD) significantly correlated with educational attainment, wealth status, religion, access to mass media, partner's education, and zone of residence. The likelihood of practicing open defecation reduced among those with formal education [aOR = 0.69, CI = 0.56-0.85], those whose partners had formal education [aOR = 0.64, CI = 0.52-0.80], women in the rich wealth quintile [aOR = 0.12, CI = 0.07-0.20], the traditionalist [aOR = 0.33, CI = 0.19-0.57], and those who had access to mass media [aOR = 0.70, CI = 0.57-0.85]. Residents in the Savannah zone had higher odds of openly defecating [aOR = 21.06, CI = 15.97-27.77]. The prevalence of open defecation is disproportionately pro-poor, which indicates that impoverished rural women are more likely to perform it. Public health initiatives should aim to close the rich-poor divide in OD practice among rural women.

A Smartwatch Step-Counting App for Older Adults: Development and Evaluation Study.

BACKGROUND: Older adults who engage in physical activity can reduce their risk of mobility impairment and disability. Short amounts of walking can improve quality of life, physical function, and cardiovascular health. Various programs have been implemented to encourage older adults to engage in physical activity, but sustaining their motivation continues to be a challenge. Ubiquitous devices, such as mobile phones and smartwatches, coupled with machine-learning algorithms, can potentially encourage older adults to be more physically active. Current algorithms that are deployed in consumer devices (eg, Fitbit) are proprietary, often are not tailored to the movements of older adults, and have been shown to be inaccurate in clinical settings. Step-counting algorithms have been developed for smartwatches, but only using data from younger adults and, often, were only validated in controlled laboratory settings. OBJECTIVE: We sought to develop and validate a smartwatch step-counting app for older adults and evaluate the algorithm in free-living settings over a long period of time. METHODS: We developed and evaluated a step-counting app for older adults on an open-source wrist-worn device (Amulet). The app includes algorithms to infer the level of physical activity and to count steps. We validated the step-counting algorithm in the lab (counting steps from a video recording, n=20) and in free-living conditions-one 2-day field study (n=6) and two 12-week field studies (using the Fitbit as ground truth, n=16). During app system development, we evaluated 4 walking patterns: normal, fast, up and down a staircase, and intermittent speed. For the field studies, we evaluated 5 different cut-off values for the algorithm, using correlation and error rate as the evaluation metrics. RESULTS: The step-counting algorithm performed well. In the lab study, for normal walking (R2=0.5), there was a stronger correlation between the Amulet steps and the video-validated steps; for all activities, the Amulet's count was on average 3.2 (2.1%) steps lower (SD 25.9) than the video-validated count. For the 2-day field study, the best parameter settings led to an association between Amulet and Fitbit (R2=0.989) and 3.1% (SD 25.1) steps lower than Fitbit, respectively. For the 12-week field study, the best parameter setting led to an R2 value of 0.669. CONCLUSIONS: Our findings demonstrate the importance of an iterative process in algorithm development before field-based deployment. This work highlights various challenges and insights involved in developing and validating monitoring systems in real-world settings. Nonetheless, our step-counting app for older adults had good performance relative to the ground truth (a commercial Fitbit step counter). Our app could potentially be used to help improve physical activity among older adults.

Superior Communication of Positive Emotions Through Nonverbal Vocalisations Compared to Speech Prosody.

The human voice communicates emotion through two different types of vocalizations: nonverbal vocalizations (brief non-linguistic sounds like laughs) and speech prosody (tone of voice). Research examining recognizability of emotions from the voice has mostly focused on either nonverbal vocalizations or speech prosody, and included few categories of positive emotions. In two preregistered experiments, we compare human listeners' (total n = 400) recognition performance for 22 positive emotions from nonverbal vocalizations (n = 880) to that from speech prosody (n = 880). The results show that listeners were more accurate in recognizing most positive emotions from nonverbal vocalizations compared to prosodic expressions. Furthermore, acoustic classification experiments with machine learning models demonstrated that positive emotions are expressed with more distinctive acoustic patterns for nonverbal vocalizations as compared to speech prosody. Overall, the results suggest that vocal expressions of positive emotions are communicated more successfully when expressed as nonverbal vocalizations compared to speech prosody. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s10919-021-00375-1.

Social Support and Common Dyadic Coping in Couples' Dyadic Management of Type II Diabetes: Protocol for an Ambulatory Assessment Application.

BACKGROUND: Type II diabetes mellitus (T2DM) is a common chronic disease. To manage blood glucose levels, patients need to follow medical recommendations for healthy eating, physical activity, and medication adherence in their everyday life. Illness management is mainly shared with partners and involves social support and common dyadic coping (CDC). Social support and CDC have been identified as having implications for people's health behavior and well-being. Visible support, however, may also be negatively related to people's well-being. Thus, the concept of invisible support was introduced. It is unknown which of these concepts (ie, visible support, invisible support, and CDC) displays the most beneficial associations with health behavior and well-being when considered together in the context of illness management in couple's everyday life. Therefore, a novel ambulatory assessment application for the open-source behavioral intervention platform MobileCoach (AAMC) was developed. It uses objective sensor data in combination with self-reports in couple's everyday life. OBJECTIVE: The aim of this paper is to describe the design of the Dyadic Management of Diabetes (DyMand) study, funded by the Swiss National Science Foundation (CR12I1_166348/1). The study was approved by the cantonal ethics committee of the Canton of Zurich, Switzerland (Req-2017_00430). METHODS: This study follows an intensive longitudinal design with 2 phases of data collection. The first phase is a naturalistic observation phase of couples' conversations in combination with experience sampling in their daily lives, with plans to follow 180 T2DM patients and their partners using sensor data from smartwatches, mobile phones, and accelerometers for 7 consecutive days. The second phase is an observational study in the laboratory, where couples discuss topics related to their diabetes management. The second phase complements the first phase by focusing on the assessment of a full discussion about diabetes-related concerns. Participants are heterosexual couples with 1 partner having a diagnosis of T2DM. RESULTS: The AAMC was designed and built until the end of 2018 and internally tested in March 2019. In May 2019, the enrollment of the pilot phase began. The data collection of the DyMand study will begin in September 2019, and analysis and presentation of results will be available in 2021. CONCLUSIONS: For further research and practice, it is crucial to identify the impact of social support and CDC on couples' dyadic management of T2DM and their well-being in daily life. Using AAMC will make a key contribution with regard to objective operationalizations of visible and invisible support, CDC, physical activity, and well-being. Findings will provide a sound basis for theory- and evidence-based development of dyadic interventions to change health behavior in the context of couple's dyadic illness management. Challenges to this multimodal sensor approach and its feasibility aspects are discussed. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/13685.

Use of Amulet in behavioral change for geriatric obesity management.

BACKGROUND: Obesity in older adults is a significant public health concern. Weight-loss interventions are known to improve physical function but risk the development of sarcopenia. Mobile health devices have the potential to augment existing interventions and, if designed accordingly, could improve one's physical activity and strength in routine physical activity interventions. METHODS AND RESULTS: We present Amulet, a mobile health device that has the capability of engaging patients in physical activity. The purpose of this article is to discuss the development of applications that are tailored to older adults with obesity, with the intention to engage and improve their health. CONCLUSIONS: Using a team-science approach, Amulet has the potential, as an open-source mobile health device, to tailor activity interventions to older adults.

Experience: Design, Development and Evaluation of a Wearable Device for mHealth Applications.

Wrist-worn devices hold great potential as a platform for mobile health (mHealth) applications because they comprise a familiar, convenient form factor and can embed sensors in proximity to the human body. Despite this potential, however, they are severely limited in battery life, storage, band-width, computing power, and screen size. In this paper, we describe the experience of the research and development team designing, implementing and evaluating Amulet - an open-hardware, open-software wrist-worn computing device - and its experience using Amulet to deploy mHealth apps in the field. In the past five years the team conducted 11 studies in the lab and in the field, involving 204 participants and collecting over 77,780 hours of sensor data. We describe the technical issues the team encountered and the lessons they learned, and conclude with a set of recommendations. We anticipate the experience described herein will be useful for the development of other research-oriented computing platforms. It should also be useful for researchers interested in developing and deploying mHealth applications, whether with the Amulet system or with other wearable platforms.

GeriActive: Wearable App for Monitoring and Encouraging Physical Activity among Older Adults.

The ability to monitor a person's level of daily activity can inform self-management of physical activity and assist in augmenting behavioral interventions. For older adults, the importance of regular physical activity is critical to reduce the risk of long-term disability. In this work, we present GeriActive, an application on the Amulet wrist-worn device that monitors in real time older adults' daily activity levels (low, moderate and vigorous), which we categorized using metabolic equivalents (METs). The app implements an activity-level detection model we developed using a linear Support Vector Machine (SVM). We trained our model using data from volunteer subjects (n=29) who performed common physical activities (sit, stand, lay down, walk and run) and obtained an accuracy of 94.3% with leave-one-subject-out (LOSO) cross-validation. We ran a week-long field study to evaluate the usability and battery life of the GeriActive system where 5 older adults wore the Amulet as it monitored their activity level. Their feedback showed that our system has the potential to be usable and useful. Our evaluation further revealed a battery life of at least 1 week. The results are promising, indicating that the app may be used for activity-level monitoring by individuals or researchers for health delivery interventions that could improve the health of older adults.

Usability Evaluation for the Amulet Wearable Device in Rural Older Adults with Obesity.

Mobile health (mHealth) interventions hold the promise of augmenting existing health promotion interventions. Older adults present unique challenges in advancing new models of health promotion using technology including sensory limitations and less experience with mHealth, underscoring the need for specialized usability testing. We use an open-source mHealth device as a case example for its integration in a newly designed health services intervention. We performed a convergent, parallel mixed-methods study including semi-structured interviews, focus groups, and questionnaires, using purposive sampling of 29 older adults, 4 community leaders and 7 clinicians in a rural setting We transcribed the data, developed codes informed by thematic analysis using inductive and deductive methods, and assessed the quantitative data using descriptive statistics. Our results suggest the importance of end-users in user-centered design of mHealth devices and that aesthetics are critically important. The prototype could potentially be feasibly integrated within health behavior interventions. Centralized dashboards were desired by all participants and ecological momentary assessment could be an important part of monitoring. Concerns of mHealth, including the prototype device, include the device's accuracy, its intrusiveness in daily life and privacy. Formative evaluations are critically important prior to deploying large-scale interventions.

Detection and Monitoring of Repetitions Using an mHealth-Enabled Resistance Band.

Sarcopenia is defined as an age-related loss of muscle mass and strength which impairs physical function leading to disability and frailty. Resistance exercises are effective treatments for sarcopenia and are critical in mitigating weight-loss induced sarcopenia in older adults attempting to lose weight. Yet, adherence to home-based regimens, which is a cornerstone to lifestyle therapies, is poor and cannot be ascertained by clinicians as no objective methods exist to determine patient compliance outside of a supervised setting. Our group developed a Bluetooth connected resistance band that tests the ability to detect exercise repetitions. We recruited 6 patients aged 65 years and older and recorded 4 specific, physical therapist-led exercises. Three blinded reviewers examined the findings and we also applied a peak Ending algorithm to the data. There were 16.6 repetitions per exercise across reviewers, with an intraclass correlation of 0.912 (95%CI: 0.853-0.953, p<0.001) between reviewers and the algorithm. Using this novel resistance band, we feasibly detected repetition of exercises in older adults.

ActivityAware: An App for Real-Time Daily Activity Level Monitoring on the Amulet Wrist-Worn Device.

Physical activity helps reduce the risk of cardiovascular disease, hypertension and obesity. The ability to monitor a person's daily activity level can inform self-management of physical activity and related interventions. For older adults with obesity, the importance of regular, physical activity is critical to reduce the risk of long-term disability. In this work, we present ActivityAware, an application on the Amulet wrist-worn device that measures daily activity levels (sedentary, moderate and vigorous) of individuals, continuously and in real-time. The app implements an activity-level detection model, continuously collects acceleration data on the Amulet, classifies the current activity level, updates the day's accumulated time spent at that activity level, logs the data for later analysis, and displays the results on the screen. We developed an activity-level detection model using a Support Vector Machine (SVM). We trained our classifiers using data from a user study, where subjects performed the following physical activities: sit, stand, lay down, walk and run. With 10-fold cross validation and leave-one-subject-out (LOSO) cross validation, we obtained preliminary results that suggest accuracies up to 98%, for n=14 subjects. Testing the ActivityAware app revealed a projected battery life of up to 4 weeks before needing to recharge. The results are promising, indicating that the app may be used for activity-level monitoring, and eventually for the development of interventions that could improve the health of individuals.