A longitudinal evaluation of the acceptability and impact of a diet diary app for older adults with age-related macular degeneration

Hakobyan, Lilit; Lumsden, Jo; Shaw, Rachel; O’Sullivan, Dympna

doi:10.1145/2935334.2935356

Cited by 20 publications

(6 citation statements)

References 25 publications

(23 reference statements)

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“…These findings are consistent with other longitudinal studies on older adults' use of modern technologies such as internet, smartphone applications, and smart speakers (Hakobyan et al, 2016;Kim & Choudhury, 2021;Nimrod & Edan, 2022;Shillair et al, 2015). These studies found that longitudinal settings are essential to give older adults time to explore new technology at their own pace (Shillair et al, 2015), fully evaluate the long-term adaptability and acceptability of the technology (Nimrod & Edan, 2022), necessary to achieve a more accurate perspective on the behavioral and perceptual changes among older adults (Hakobyan et al, 2016). Furthermore, this research demonstrated the importance of advancing longitudinal studies to evaluate parameters influencing SARs assimilation.…”

Section: Simultaneous Exploration Of Uses Constraints and Outcomes In...supporting

confidence: 91%

Towards a Holistic Approach to Studying Human–Robot Interaction in Later Life

Zafrani

Nimrod

2018

The Gerontologist

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Additional studies are needed to more fully understand what makes HRI successful. The model presented here suggests scholars to conduct theory-driven research, and distinguish among various segments of older users, different types of robots, and group and individual HRI. It also proposes paying greater attention to the users' cultural, physical and social environment, and application of longitudinal and simultaneous examination of uses, outcomes, and constraints.

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Section: Simultaneous Exploration Of Uses Constraints and Outcomes In...supporting

confidence: 91%

Towards a Holistic Approach to Studying Human–Robot Interaction in Later Life

Zafrani

Nimrod

2018

The Gerontologist

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“…However, the dietary information collected through these methods often lack representation because of insufficient data or problems with memory retrieval [46]. Researchers have tried to improve these dietary tracking methods with direct inputs of food intake, but these methods are cumbersome for long-term use [47]. Many social forum users share their dietary information through social media such as Twitter and Instagram [48,49].…”

Section: Posts Sharing or Asking For Dietary Informationmentioning

confidence: 99%

Exploring Abnormal Behavior Patterns of Online Users With Emotional Eating Behavior: Topic Modeling Study

Hwang¹,

Kim²,

Choi³

et al. 2020

J Med Internet Res

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Background Emotional eating (EE) is one of the most significant symptoms of various eating disorders. It has been difficult to collect a large amount of behavioral data on EE; therefore, only partial studies of this symptom have been conducted. To provide adequate support for online social media users with symptoms of EE, we must understand their behavior patterns to design a sophisticated personalized support system (PSS). Objective This study aimed to analyze the behavior patterns of emotional eaters as the first step to designing a personalized intervention system. Methods The machine learning (ML) framework and Latent Dirichlet Allocation (LDA) topic modeling tool were used to collect and analyze behavioral data on EE. Data from a subcommunity of Reddit, /r/loseit, were analyzed. This dataset included all posts and feedback from July 2014 to May 2018, comprising 185,950 posts and 3,528,107 comments. In addition, deleted and improperly collected data were eliminated. Stochastic gradient descent–based ML classifier with an accuracy of 90.64% was developed to collect refined behavioral data of online users with EE behaviors. The expert group that labeled the dataset to train the ML classifiers included a medical doctor specializing in EE diagnosis and a nutritionist with profound knowledge of EE behavior. The experts labeled 5126 posts as EE (coded as 1) or others (coded as 0). Finally, the topic modeling process was conducted with LDA. Results The following 4 macroperspective topics of online EE behaviors were identified through linguistic evidence regarding each topic: addressing feelings, sharing physical changes, sharing and asking for dietary information, and sharing dietary strategies. The 5 main topics of feedback were dietary information, compliments, consolation, automatic bot feedback, and health information. The feedback topic distribution significantly differed depending on the type of EE behavior (overall P<.001). Conclusions This study introduces a data-driven approach for analyzing behavior patterns of social website users with EE behaviors. We discovered the possibility of the LDA topic model as an exploratory user study method for abnormal behaviors in medical research. We also investigated the possibilities of ML- and topic modeling–based classifiers to automatically categorize text-based behavioral data, which could be applied to personalized medicine in future research.

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“…The literature has explored older adults’ attitudes towards the use of technology for self-management. Research has examined wellness, 12 , 13 social well-being, 14 functional abilities, 15 physical activity, 16 physical rehabilitation, 17 medication management 18 , 19 and chronic disease self-management. 20 – 22 Older adults are willing to change their behaviour to adopt digital self-management.…”

Section: Introductionmentioning

confidence: 99%

Home-based digital health technologies for older adults to self-manage multiple chronic conditions: A data-informed analysis of user engagement from a longitudinal trial

et al. 2022

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Background Ageing populations are resulting in higher prevalence of people with multiple chronic conditions (multimorbidity). Digital health platforms have great potential to support self-management of multimorbidity, increasing a person's awareness of their health and well-being, supporting a better understanding of diseases and encouraging behaviour change. However, little research has explored the long-term engagement of older adults with such digital interventions. Methods The aim of this study is to analyse how 60 older adults with multimorbidity engaged with digital symptom and well-being monitoring through a digital health platform over a period of approximately 12 months. Data analysis focused on user retention, frequency of monitoring, intervals in monitoring and patterns of daily engagement. Results Our findings show that the overall engagement with the digital health platform was high, with more than 80% of participants using the technology devices for over 200 days. The submission frequency for symptom parameters (e.g. blood glucose (BG), blood pressure (BP), etc.) was between three and four times per week which was higher than that of self-report (2.24) and weight (2.84). Submissions of exercise (6.12) and sleep (5.67) were more frequent. The majority of interactions happened in the morning time. The most common time of submission for symptom parameters was 10 am, whereas 8 am was the most common time for weight measurements. Conclusions The findings indicate the patterns of engagement of older adults with complex chronic diseases with digital home-based self-management systems.

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A longitudinal evaluation of the acceptability and impact of a diet diary app for older adults with age-related macular degeneration

Cited by 20 publications

References 25 publications

Towards a Holistic Approach to Studying Human–Robot Interaction in Later Life

Towards a Holistic Approach to Studying Human–Robot Interaction in Later Life

Exploring Abnormal Behavior Patterns of Online Users With Emotional Eating Behavior: Topic Modeling Study

Home-based digital health technologies for older adults to self-manage multiple chronic conditions: A data-informed analysis of user engagement from a longitudinal trial

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