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Impact of a Collective Intelligence Tailored Messaging System on Smoking Cessation: The Perspect Randomized Experiment

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ABSTRACT

Background: Outside health care, content tailoring is driven algorithmically using machine learning compared to the rule-based approach used in current implementations of computer-tailored health communication (CTHC) systems. A special class of machine learning systems (“recommender systems”) are used to select messages by combining the collective intelligence of their users (ie, the observed and inferred preferences of users as they interact with the system) and their user profiles. However, this approach has not been adequately tested for CTHC.

Objective: Our aim was to compare, in a randomized experiment, a standard, evidence-based, rule-based CTHC (standard CTHC) to a novel machine learning CTHC: Patient Experience Recommender System for Persuasive Communication Tailoring (PERSPeCT). We hypothesized that PERSPeCT will select messages of higher influence than our standard CTHC system. This standard CTHC was proven effective in motivating smoking cessation in a prior randomized trial of 900 smokers (OR 1.70, 95% CI 1.03-2.81).

Methods: PERSPeCT is an innovative hybrid machine learning recommender system that selects and sends motivational messages using algorithms that learn from message ratings from 846 previous participants (explicit feedback), and the prior explicit ratings of each individual participant. Current smokers (N=120) aged 18 years or older, English speaking, with Internet access were eligible to participate. These smokers were randomized to receive either PERSPeCT (intervention, n=74) or standard CTHC tailored messages (n=46). The study was conducted between October 2014 and January 2015. By randomization, we compared daily message ratings (mean of smoker ratings each day). At 30 days, we assessed the intervention’s perceived influence, 30-day cessation, and changes in readiness to quit from baseline.

Results: The proportion of days when smokers agreed/strongly agreed (daily rating ≥4) that the messages influenced them to quit was significantly higher for PERSPeCT (73%, 23/30) than standard CTHC (44%, 14/30, P=.02). Among less educated smokers (n=49), this difference was even more pronounced for days strongly agree (intervention: 77%, 23/30; comparison: 23%, 7/30, P<.001). There was no significant difference in the frequency which PERSPeCT randomized smokers agreed or strongly agreed that the intervention influenced them to quit smoking (P=.07) and use nicotine replacement therapy (P=.09). Among those who completed follow-up, 36% (20/55) of PERSPeCT smokers and 32% (11/34) of the standard CTHC group stopped smoking for one day or longer (P=.70).

Conclusions: Compared to standard CTHC with proven effectiveness, PERSPeCT outperformed in terms of influence ratings and resulted in similar cessation rates.

Clinicaltrial: Clinicaltrials.gov NCT02200432; https://clinicaltrials.gov/ct2/show/NCT02200432 (Archived by WebCite at http://www.webcitation.org/6lEJY1KEd)

No MeSH data available.


The Patient Experience Recommender System for Persuasive Communication Tailoring (PERSPeCT) recommender computer-tailored health communication system.
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figure1: The Patient Experience Recommender System for Persuasive Communication Tailoring (PERSPeCT) recommender computer-tailored health communication system.

Mentions: For PERSPeCT, we chose a hybrid approach because they merge the strengths of content-based and collaborative filtering recommender systems [22]. Thus, they can potentially benefit from expert-driven rules (content-based) and the recommender algorithms. We used the following data sources to develop the models for our algorithm: (1) metadata description of the messages, (2) implicit, and (3) explicit user feedback data (Figure 1). As explained previously, our coding of the messages by the readiness to quit categories is an example of metadata. In preparation for PERSPeCT, we expanded this metadata to include constructs from multiple behavioral theories, such as the SCT, the Transtheoretical Model, and the Theory of Reasoned Action [24]. We also coded the messages for content that may be pertinent to a specific user, including health and lifestyle status, health issues, and treatment options. In total, 40% (102/261) of messages had motivational content, such as reasons to quit, and 53% (139/261) of messages had information about behavioral treatments, such as substitution and distraction.


Impact of a Collective Intelligence Tailored Messaging System on Smoking Cessation: The Perspect Randomized Experiment
The Patient Experience Recommender System for Persuasive Communication Tailoring (PERSPeCT) recommender computer-tailored health communication system.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC5120237&req=5

figure1: The Patient Experience Recommender System for Persuasive Communication Tailoring (PERSPeCT) recommender computer-tailored health communication system.
Mentions: For PERSPeCT, we chose a hybrid approach because they merge the strengths of content-based and collaborative filtering recommender systems [22]. Thus, they can potentially benefit from expert-driven rules (content-based) and the recommender algorithms. We used the following data sources to develop the models for our algorithm: (1) metadata description of the messages, (2) implicit, and (3) explicit user feedback data (Figure 1). As explained previously, our coding of the messages by the readiness to quit categories is an example of metadata. In preparation for PERSPeCT, we expanded this metadata to include constructs from multiple behavioral theories, such as the SCT, the Transtheoretical Model, and the Theory of Reasoned Action [24]. We also coded the messages for content that may be pertinent to a specific user, including health and lifestyle status, health issues, and treatment options. In total, 40% (102/261) of messages had motivational content, such as reasons to quit, and 53% (139/261) of messages had information about behavioral treatments, such as substitution and distraction.

View Article: PubMed Central - HTML - PubMed

ABSTRACT

Background: Outside health care, content tailoring is driven algorithmically using machine learning compared to the rule-based approach used in current implementations of computer-tailored health communication (CTHC) systems. A special class of machine learning systems (“recommender systems”) are used to select messages by combining the collective intelligence of their users (ie, the observed and inferred preferences of users as they interact with the system) and their user profiles. However, this approach has not been adequately tested for CTHC.

Objective: Our aim was to compare, in a randomized experiment, a standard, evidence-based, rule-based CTHC (standard CTHC) to a novel machine learning CTHC: Patient Experience Recommender System for Persuasive Communication Tailoring (PERSPeCT). We hypothesized that PERSPeCT will select messages of higher influence than our standard CTHC system. This standard CTHC was proven effective in motivating smoking cessation in a prior randomized trial of 900 smokers (OR 1.70, 95% CI 1.03-2.81).

Methods: PERSPeCT is an innovative hybrid machine learning recommender system that selects and sends motivational messages using algorithms that learn from message ratings from 846 previous participants (explicit feedback), and the prior explicit ratings of each individual participant. Current smokers (N=120) aged 18 years or older, English speaking, with Internet access were eligible to participate. These smokers were randomized to receive either PERSPeCT (intervention, n=74) or standard CTHC tailored messages (n=46). The study was conducted between October 2014 and January 2015. By randomization, we compared daily message ratings (mean of smoker ratings each day). At 30 days, we assessed the intervention’s perceived influence, 30-day cessation, and changes in readiness to quit from baseline.

Results: The proportion of days when smokers agreed/strongly agreed (daily rating ≥4) that the messages influenced them to quit was significantly higher for PERSPeCT (73%, 23/30) than standard CTHC (44%, 14/30, P=.02). Among less educated smokers (n=49), this difference was even more pronounced for days strongly agree (intervention: 77%, 23/30; comparison: 23%, 7/30, P<.001). There was no significant difference in the frequency which PERSPeCT randomized smokers agreed or strongly agreed that the intervention influenced them to quit smoking (P=.07) and use nicotine replacement therapy (P=.09). Among those who completed follow-up, 36% (20/55) of PERSPeCT smokers and 32% (11/34) of the standard CTHC group stopped smoking for one day or longer (P=.70).

Conclusions: Compared to standard CTHC with proven effectiveness, PERSPeCT outperformed in terms of influence ratings and resulted in similar cessation rates.

Clinicaltrial: Clinicaltrials.gov NCT02200432; https://clinicaltrials.gov/ct2/show/NCT02200432 (Archived by WebCite at http://www.webcitation.org/6lEJY1KEd)

No MeSH data available.