Enhancing dieters' perseverance in adversity: How counterfactual thinking increases use of digital health tracking tools

Appetite ◽  
2021 ◽  
Vol 164 ◽  
pp. 105261
Author(s):  
Kai-Yu Wang ◽  
Melissa G. Bublitz ◽  
Guangzhi (Terry) Zhao
2020 ◽  
Author(s):  
Dianna Alva ◽  
Lourdes Martinez ◽  
Noe Crespo ◽  
Eric R. Walsh-Buhi

BACKGROUND For many emerging adults, the transition to college can mark a critical life stage with increased risk for weight gain and obesity, the results of which may bring early onset of chronic disease and mortality. Weight gain among college students is a well-documented phenomenon, with current estimates placing the prevalence of obesity among college students in the United States at around 30%. Digital wearable devices may offer a promising solution toward further addressing the issue of weight gain and obesity. Wearables are defined as devices, accessories or clothing items utilizing computer and electronic technology to track, monitor and/or detect symptoms, behaviors or other health outcomes, and include pedometers, Fitbits or other activity/sleep monitoring bands or devices and smart phone apps used for health tracking and goal-setting. OBJECTIVE This paper reports on college students’ engagement with health tracking wearable devices including their specific device uses, intentions of use, satisfaction, and perception of benefits. We further explored characteristics of students who initiate and sustain use of their devices for longer than six months. METHODS Participants included undergraduate students at a large, urban public university in the Southwestern USA in 2017. A cross-sectional survey was administered and assessed college students’ use and perceptions of health tracking wearables. Method: Descriptive statistics summarize the data on participants’ common responses to device use and perceptions. Bivariate correlations were employed to identify characteristics of respondents who initiate and sustain engagement. RESULTS 86% of respondents currently or previously owned a digital health wearable device. Less than 1% reported that they have never owned a device and would not be interested in ever owning one. Cell phone apps were the most commonly used device, followed by fitness tracking bands. Devices were most frequently used to track steps or exercise routine (81%), diet, calories, or weight (54%), and sleep (26%). About half of wearable users (48%) reported that they share data they collect, socially, from their wearables. It was found that high perceived usefulness and satisfaction, exercise levels, and high perception of social norms were statistically significantly correlated with sustained use. CONCLUSIONS In using wearables among college students to promote long-term healthy behavior changes, there are opportunities to utilize the critical roles that perceived social norms and perceived usefulness of a device can have on one’s long-term behaviors while emphasizing any enjoyable user experiences created by these devices.


2019 ◽  
Vol 2 (1) ◽  
Author(s):  
Elias Chaibub Neto ◽  
Abhishek Pratap ◽  
Thanneer M. Perumal ◽  
Meghasyam Tummalacherla ◽  
Phil Snyder ◽  
...  

Abstract Collection of high-dimensional, longitudinal digital health data has the potential to support a wide-variety of research and clinical applications including diagnostics and longitudinal health tracking. Algorithms that process these data and inform digital diagnostics are typically developed using training and test sets generated from multiple repeated measures collected across a set of individuals. However, the inclusion of repeated measurements is not always appropriately taken into account in the analytical evaluations of predictive performance. The assignment of repeated measurements from each individual to both the training and the test sets (“record-wise” data split) is a common practice and can lead to massive underestimation of the prediction error due to the presence of “identity confounding.” In essence, these models learn to identify subjects, in addition to diagnostic signal. Here, we present a method that can be used to effectively calculate the amount of identity confounding learned by classifiers developed using a record-wise data split. By applying this method to several real datasets, we demonstrate that identity confounding is a serious issue in digital health studies and that record-wise data splits for machine learning- based applications need to be avoided.


PLoS Biology ◽  
2017 ◽  
Vol 15 (1) ◽  
pp. e2001402 ◽  
Author(s):  
Xiao Li ◽  
Jessilyn Dunn ◽  
Denis Salins ◽  
Gao Zhou ◽  
Wenyu Zhou ◽  
...  

2003 ◽  
Vol 62 (4) ◽  
pp. 209-218
Author(s):  
A. N’gbala ◽  
N. R. Branscombe

When do causal attribution and counterfactual thinking facilitate one another, and when do the two responses overlap? Undergraduates (N = 78) both explained and undid, in each of two orders, events that were described either with their potential causes or not. The time to perform either response was recorded. Overall, mutation response times were shorter when performed after an attribution was made than before, while attribution response times did not vary as a consequence of sequence. Depending on whether the causes of the target events were described in the scenario or not, respondents undid the actor and assigned causality to another antecedent, or pointed to the actor for both responses. These findings suggest that counterfactual mutation is most likely to be facilitated by attribution, and that mutation and attribution responses are most likely to overlap when no information about potential causes of the event is provided.


1999 ◽  
Author(s):  
Katie Pasco ◽  
Heather Sakai ◽  
Amanda Woodside ◽  
Karen P. Leith ◽  
Julie Robinson

2006 ◽  
Author(s):  
Tarika Daftary ◽  
Melissa A. Berry Cahoon

Sign in / Sign up

Export Citation Format

Share Document