Can digital personal assistants persuade people to exercise?

Paay, Jeni; Kjeldskov, Jesper; Papachristos, Eleftherios; Hansen, Kathrine Maja; Jørgensen, Tobias Bornakke; Overgaard, Katrine Leth

doi:10.1080/0144929x.2020.1814412

Cited by 14 publications

(16 citation statements)

References 56 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The included studies were conducted in various settings. In total, 40% (8/20) were conducted in a laboratory setting [ 32 , 35 , 37 , 39 - 41 , 48 , 49 ], 25% (5/20) were conducted in a home setting [ 3 , 32 , 33 , 43 , 51 ], and 10% (2/20) were conducted in a hospital setting [ 42 , 47 ]. Laboratory setting means that the study was conducted in an environment where researchers installed smart home sensors in a laboratory for data collection instead of in a real home setting, which has basic rooms and equipment for daily living.…”

Section: Resultsmentioning

confidence: 99%

“…We used the accuracy of the evaluated smart home technologies (eg, activity detection and recognition sensors) in the quantitative studies and the subjective judgment or conclusions of the authors in the qualitative studies to determine the effectiveness of a given technology. As shown in Table 1 , a total of 75% (15/20) of the studies [ 3 , 32 - 37 , 39 , 40 , 43 , 45 , 46 , 48 , 49 , 51 ] had a positive outcome, 10% (2/20) [ 38 , 41 ] had a mixed outcome, and 15% (3/20) [ 42 , 44 , 47 ] had an indeterminate outcome.…”

Section: Resultsmentioning

confidence: 99%

“…For example, Li et al [ 39 ] and Syed et al [ 45 ] focused on the recognition of physical activity such as cycling, walking, running, jumping, standing, sitting, and turning. Moreover, few studies (6/20, 30%) specifically focused on therapeutic exercises [ 32 , 33 , 37 , 42 ] and bodyweight exercises [ 38 , 41 ], which can also be considered physical activity.…”

Section: Discussionmentioning

confidence: 99%

See 2 more Smart Citations

Using Smart Home Technologies to Promote Physical Activity Among the General and Aging Populations: Scoping Review

Oyibo¹,

Wang²,

Morita³

2023

J Med Internet Res

View full text Add to dashboard Cite

Background Health-monitoring smart homes are becoming popular, with experts arguing that 9-to-5 health care services might soon become a thing of the past. However, no review has explored the landscape of smart home technologies that aim to promote physical activity and independent living among a wide range of age groups. Objective This review aims to map published studies on smart home technologies aimed at promoting physical activity among the general and aging populations to unveil the state of the art, its potential, and the research gaps and opportunities. Methods Articles were retrieved from 6 databases (PubMed, CINAHL, Scopus, IEEE Xplore, ACM Library, and Web of Science). The criteria for inclusion were that the articles must be user studies that dealt with smart home or Active Assisted Living technologies and physical activity, were written in English, and were published in peer-reviewed journals. In total, 3 researchers independently and collaboratively assessed the eligibility of the retrieved articles and elicited the relevant data and findings using tables and charts. Results This review synthesized 20 articles that met the inclusion criteria, 70% (14/20) of which were conducted between 2018 and 2020. Three-quarters of the studies (15/20, 75%) were conducted in Western countries, with the United States accounting for 25% (5/20). Activities of daily living were the most studied (9/20, 45%), followed by physical activity (6/20, 30%), therapeutic exercise (4/20, 20%), and bodyweight exercise (1/20, 5%). K-nearest neighbor and naïve Bayes classifier were the most used machine learning algorithms for activity recognition, with at least 10% (2/20) of the studies using either algorithm. Ambient and wearable technologies were equally studied (8/20, 40% each), followed by robots (3/20, 15%). Activity recognition was the most common goal of the evaluated smart home technologies, with 55% (11/20) of the studies reporting it, followed by activity monitoring (7/20, 35%). Most studies (8/20, 40%) were conducted in a laboratory setting. Moreover, 25% (5/20) and 10% (2/20) were conducted in a home and hospital setting, respectively. Finally, 75% (15/20) had a positive outcome, 15% (3/20) had a mixed outcome, and 10% (2/20) had an indeterminate outcome. Conclusions Our results suggest that smart home technologies, especially digital personal assistants, coaches, and robots, are effective in promoting physical activity among the young population. Although only few studies were identified among the older population, smart home technologies hold bright prospects in assisting and aiding older people to age in place and function independently, especially in Western countries, where there are shortages of long-term care workers. Hence, there is a need to do more work (eg, cross-cultural studies and randomized controlled trials) among the growing aging population on the effectiveness and acceptance of smart home technologies that aim to promote physical activity.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Using Smart Home Technologies to Promote Physical Activity Among the General and Aging Populations: Scoping Review

Oyibo¹,

Wang²,

Morita³

2023

J Med Internet Res

View full text Add to dashboard Cite

show abstract

“…In commercial communication, persuasion can take the forms of notifications and messages aimed at leveraging consumer emotional responses to motivate action; such notifications and messages can be used in branding management and brand promotion (Mingli Zhang, Hu, Guo, & Liu, 2017). Persuasive technologies are used in many application areas, including advertising and marketing (Edwards, McDonald, Zhao, & Humphries, 2014;King, Lazard, & White, 2020;Orji, Reilly, Oyibo, & Orji, 2019;Paay et al, 2020;Ruijten, 2020).…”

Section: Introductionmentioning

confidence: 99%

Which Message? Which Channel? Which Customer? - Exploring Response Rates in Multi-Channel Marketing Using Short-Form Advertising

Marzouk

Salminen

Zhang

et al. 2021

Data and Information Management

View full text Add to dashboard Cite

Formulating short-form advertising messages with little ad content that work and choosing high-performing channels to disseminate them are persistent challenges in multichannel marketing. Drawing on the persuasive systems design (PSD) model, we experimented with 33,848 actual customers of an international telecom company. In a real-life setting, we compared the effectiveness of three persuasion strategies (rational, emotional, and social) tested in three marketing channels (short message service (SMS), social media advertising, and mobile application), evaluating their effect on influencing customers to purchase international mobile phone credits. Results suggest that companies should send rational messages when using short-form advertising messages regardless of the channel to achieve higher response rates. Findings further show that certain customer characteristics are predictive of positive responses and differ by channel but not by message type. Findings from crowdsourced evaluations also indicate that people noticeably disagree on what persuasive strategy was applied to these short messages, indicating that consumers are not well-equipped to identify persuasive strategies or that what advertisers see as a “pure” strategy actually involves elements from multiple strategies as interpreted by consumers. The results have implications for the theoretical understanding of persuasive short-form commercial messaging in multichannel marketing and practical insights for advertising within a limited amount of space and attention afforded by many digital channels.

show abstract

“…The resurgence of digital mental health treatments is being fueled by a renaissance in AI technologies. Conversational agents or Chatbots are AI-enabled software systems that can communicate with people using natural language across a text or voice-based interaction [10]. This technology has been constantly developing and is currently utilized in digital assistants such as Apples's Siri, Yandex's Alice, Amazon's Alexa and other virtual assistants, as well as consumer interfaces in electronic shopping and online banking [11].…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence-Enabled Chatbots in Mental Health: A Systematic Review

Omarov¹,

Narynov²,

Zhumanov³

2023

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

Clinical applications of Artificial Intelligence (AI) for mental health care have experienced a meteoric rise in the past few years. AIenabled chatbot software and applications have been administering significant medical treatments that were previously only available from experienced and competent healthcare professionals. Such initiatives, which range from "virtual psychiatrists" to "social robots" in mental health, strive to improve nursing performance and cost management, as well as meeting the mental health needs of vulnerable and underserved populations. Nevertheless, there is still a substantial gap between recent progress in AI mental health and the widespread use of these solutions by healthcare practitioners in clinical settings. Furthermore, treatments are frequently developed without clear ethical concerns. While AI-enabled solutions show promise in the realm of mental health, further research is needed to address the ethical and social aspects of these technologies, as well as to establish efficient research and medical practices in this innovative sector. Moreover, the current relevant literature still lacks a formal and objective review that specifically focuses on research questions from both developers and psychiatrists in AI-enabled chatbotpsychologists development. Taking into account all the problems outlined in this study, we conducted a systematic review of AI-enabled chatbots in mental healthcare that could cover some issues concerning psychotherapy and artificial intelligence. In this systematic review, we put five research questions related to technologies in chatbot development, psychological disorders that can be treated by using chatbots, types of therapies that are enabled in chatbots, machine learning models and techniques in chatbot psychologists, as well as ethical challenges.

show abstract

Can digital personal assistants persuade people to exercise?

Cited by 14 publications

References 56 publications

Using Smart Home Technologies to Promote Physical Activity Among the General and Aging Populations: Scoping Review

Using Smart Home Technologies to Promote Physical Activity Among the General and Aging Populations: Scoping Review

Which Message? Which Channel? Which Customer? - Exploring Response Rates in Multi-Channel Marketing Using Short-Form Advertising

Artificial Intelligence-Enabled Chatbots in Mental Health: A Systematic Review

Contact Info

Product

Resources

About