Table 3.

Methods for tailoring message content to match users’ specific conditions.

Study	Tailored method
Mulvaney et al [35], 2012	Timing and frequency Evaluation before the intervention (top 3 barriers in the Diabetes Adherence Barriers Assessment)
Osborn and Mulvaney [36], 2013	Timing and frequency Evaluation before the intervention (top 3 barriers in the Diabetes Medication Knowledge Questionnaire, Medicines for Diabetes Questionnaire, Barriers to Diabetes Adherence measure, and Medication Adherence Self-Efficacy Scale)
Bobrow et al [38], 2014	Individual information (name and scheduled appointment)
Gatwood et al [39], 2014	Timing and frequency Individual information (name, age, and medication regimens) Evaluation before the intervention (patient level [low, medium, or high] categorized by the health beliefs model and self-determination theory)
Buis et al [41], 2015	Timing and frequency Individual information (name)
Bobrow et al [42], 2016	Individual information (name, birthday, and scheduled appointment)
Nelson et al [22], 2016	Individual information (medication names) Evaluation before the intervention (top 4 barriers in the Information-motivation-behavioral skills model evaluation) Evaluation during the intervention (daily survey on medication adherence)
Haramiova et al [45], 2017	Timing and number of daily reminders Individual information (medication names, dosage, and frequency)
Huo et al [46], 2017	Individual information (name and smoking status)
Prayaga et al [49], 2018	Users’ responses (the refill process and their identity)
Angellotti et al [50], 2019	Evaluation before the intervention (behavioral economics, such as limiting choice overload and setting goal gradients, and patient-specified personal health goals identified in the baseline questionnaire)
Farmer et al [52], 2019	Individual information (time since starting new glucose, blood pressure and cholesterol-lowering medication, and smoking status)
Huo et al [54], 2019	Individual information (name and smoking status)
Kassavou et al [55], 2019	Timing and frequency Individual information (name and medication name) Evaluation during the intervention (adjustment questionnaire and prescription plan data, considering the levels of intentional nonadherence and nonintentional nonadherence)
Waller et al [56], 2019	Individual information (name and smoking status)
Zheng et al [57], 2019	Individual information (name and smoking status)
Adu et al [58], 2020	Evaluation during the intervention (self-monitoring blood sugar)
Champoux et al [61], 2020	Evaluation during the intervention (self-monitoring blood pressure)
Li et al [64], 2020	Individual information (name) Other (different greetings according to the time and day that the SMS text message was sent, unique content each time)
Owolabi et al [65], 2020	Timing and frequency Individual information (name)
Sadanshiv et al [66], 2020	Timing and frequency
Schoenthaler et al [67], 2020	Evaluation during the intervention (daily patient-reported outcome assessments) Users’ responses (response rate)
Campos et al [69], 2021	Evaluation during the intervention (adherence checkpoint question message)
Farmer et al [70], 2021	Timing and frequency Individual information (favor language)
Leon et al [72], 2021	Timing and frequency Individual information (favor language)
Bartlett et al [74], 2022	Timing and frequency Users’ responses (users’ preference to “like” or “dislike” the message)
Bhandari et al [75], 2022	Individual information (smoking status and alcohol use status)
Levin et al [78], 2022	Individual information (name)
Mandal et al [79], 2022	Timing and frequency Individual information (favor language) Evaluation before the intervention (healthy living goal)
Van Emmenis et al [81], 2022	Timing and frequency
SPRINT INDIA trial collaborators [83], 2023	Timing and frequency
Zamanillo-Campos et al [84], 2023	Individual information (clinical data such as the presence of other chronic diseases or diabetes-related complications)
Zamanillo-Campos et al [85], 2024	Individual information (clinical data, such as the presence of other chronic diseases or diabetes-related complications)