Explanatory trials are designed to assess intervention efficacy under ideal conditions, while pragmatic trials are designed to assess whether research-proven interventions are effective in “real-world” settings without substantial research support.We compared two trials (Hyperlink 1 and 3) that tested a pharmacist-led telehealth intervention in adults with uncontrolled hypertension. We applied PRagmatic Explanatory Continuum Indicator Summary-2 (PRECIS-2) scores to describe differences in the way these studies were designed and enrolled study-eligible participants, and the effect of these differences on participant characteristics and adherence to study interventions.>140/90 mm Hg) than in Hyperlink 3 (>150/95 mm Hg), mean baseline BP was 148/85 mm Hg in Hyperlink 1 and 158/92 mm Hg in Hyperlink 3.PRECIS-2 scores demonstrated that Hyperlink 1 was more explanatory and Hyperlink 3 more pragmatic. Recruitment for Hyperlink 1 was conducted by study staff, and 2.9% of potentially eligible patients enrolled. Enrollees were older, and more likely to be male and White than non-enrollees. Study staff scheduled the initial pharmacist visit and adherence to attending this visit was 98%. Conversely for Hyperlink 3, recruitment was conducted by clinic staff at routine encounters and 81% of eligible patients enrolled. Enrollees were younger, and less likely to be male and White than non-enrollees. Study staff did not assist with scheduling the initial pharmacist visit and adherence to attending this visit was only 27%. Compared to Hyperlink 1, patients in Hyperlink 3 were more likely to be female, and Asian or Black, had lower socioeconomic indicators, and were more likely to have comorbidities. Owing to a lower BP for eligibility in Hyperlink 1 , and demonstrated that identification and enrollment of a high proportion of study-eligible subjects could be done by usual primary care clinic staff. However, the trade-off was much lower adherence to the telehealth intervention than in Hyperlink 1, which is likely to reflect uptake under real-word conditions and substantially dilute intervention effect on BP.The Hyperlink 1 study (NCT00781365) and the Hyperlink 3 study (NCT02996565) are registered at ClinicalTrials.gov. Explanatory trials are intended to test interventions with the main goal of demonstrating their efficacy under ideal conditions, while pragmatic trials are intended to test whether research-proven efficacious interventions are effective in “real-world” settings without extra research support for recruitment, adherence, retention, and fidelity to interventions. Clinical trials should be designed to fit their specific purpose and can be scored across nine domains on a continuum from “very explanatory” to “very pragmatic” using the PRagmatic Explanatory Continuum Indicator Summary-2 (PRECIS-2) tool . Key decIn this paper, we illustrate some of the major differences between explanatory and pragmatic clinical trial designs using two projects that addressed the same research question: to compare the effects on blood pressure (BP) of (1) a pharmacist-led telehealth intervention in adults with uncontrolled hypertension with 2) clinic-based primary care. Home BP monitoring combined with care management by a pharmacist or nurse has been shown to lower BP and improve control of hypertension  clinic-b. Like moThus, Hyperlink 3 was designed as a larger-scale comparison of the telehealth intervention program with clinic-based care and it intentionally included many more pragmatic design elements. This paper compares the explanatory and pragmatic design choices in Hyperlink 1 and Hyperlink 3 and illustrates how the pragmatic design of Hyperlink 3 affected enrollment, participant characteristics, and adherence to the study interventions in important ways. The comparison highlights some of the dilemmas in designing pragmatic trials and interpreting their results.The methods for the two trials have been described in detail , 17, witBoth Hyperlink trials were conducted at HealthPartners, a nonprofit integrated health system in Minnesota and western Wisconsin serving 1.8 million health plan members and 1.2 million patients with a wide variety of insurance coverage. HealthPartners expanded by acquiring several health systems between the two trials, thus expanding the number of potentially eligible clinics. It currently includes a multispecialty group practice of more than 1800 physicians, 25 medication therapy management (MTM) pharmacists, eight hospitals, and 55 primary care clinics. Clinics were eligible to participate in both trials if they had an MTM pharmacist onsite at least one half-day per week. Hyperlink 3 had the additional eligibility requirement that clinics use standardized methods to measure BP with validated oscillometric BP monitors at the time of study startup. In both trials, all eligible clinics agreed to participate (16 clinics in Hyperlink 1 and 21 clinics in Hyperlink 3). Hyperlink 1 started in 2009 and Hyperlink 3 in 2017.In Hyperlink 1, recruitment was carried out by research staff, starting with identifying 15,549 potentially eligible patients based on BP recorded in the electronic health record (EHR) during care, followed by postal mailings and telephone and research clinic screening for eligibility and recruitment of interested respondents by research staff. Patients were enrolled after meeting the eligibility criteria and giving informed consent.In Hyperlink 3, the goal was to enroll patients during a primary care visit without the need for research staff and thus to enroll patients who were representative of the entire eligible target population. Eligibility for Hyperlink 3 was evaluated using automated real-time algorithms that were triggered upon BP entry into the EHR during primary care office encounters in study clinics. For eligible patients, a best practice alert automatically prompted the medical assistant to set up a referral order for hypertension follow-up for the primary care professional (PCP) to review and sign. The referral order defaulted to in-person follow-up in 1–2 weeks depending on the clinic’s randomization status . The medical assistant and PCP were encouraged but not required to follow through on the referral prompts. PCPs were able to change the type or timing of follow-up from the defaulted choice on the referral order if they felt that a different choice was best for an individual patient. Telehealth care with home BP telemonitoring was only available for patients in telehealth clinics and was offered to patients at a subsequent MTM pharmacist visit. Enrollment in Hyperlink 3 was defined by an eligible patient having a signed hypertension referral order regardless of whether the default follow-up was kept or changed. The enrollment date was the primary care encounter during which the referral was signed.The HealthPartners Institutional Review Board (IRB) reviewed both study protocols. For Hyperlink 1, written informed consent was included in the protocol and obtained at the first research clinic visit. For Hyperlink 3, the IRB approved a partial waiver of informed consent for enrolling patients in the study because (1) the interventions did not pose additional safety risks compared to routine care for hypertension, and (2) the study could not have been practicably carried out had written informed consent been required. Enrolled patients were later contacted to complete surveys and the mailed cover letter and telephone scripts included the elements of informed consent, including that completing the survey implied their consent to use their data in the study.>140/90 mm Hg at the two most recent visits. Further screening by telephone and medical record review excluded patients who were pregnant or had recent vascular events, advanced kidney disease, or heart failure. The average of three BP standardized measurements in the research clinic had to be >140/90 mm Hg or >130/80 if the patient had diabetes or chronic kidney disease (CKD). There was no explicit exclusion for hospice or nursing home residence.Eligibility for Hyperlink 1 started at age 21 with no upper age limit. Patients were considered potentially eligible and were sent recruitment letters if they had at least two primary care outpatient encounters in the previous 12 months with BP > 150 mm Hg or DBP > 95 mm Hg in the first BP and in a repeated BP within the current encounter as well as the last recorded BP at the most recent previous encounter. The higher BP criteria for Hyperlink 3 for uncontrolled BP were rooted in the pragmatic design and were based on the estimated sample size (at least 1000 per randomized group), the estimated number of eligible patients per clinic, and the capacity of MTM pharmacists in telehealth clinics to accommodate additional follow-up referrals. Study exclusions were also similar to hypertension quality measures: pregnancy, stage 5 CKD, hospice care, and permanent nursing home residence.Most of the eligibility criteria for Hyperlink 3 were chosen to reflect the denominator population that is included for hypertension quality measures , 19. PatIn Hyperlink 1, there was no effort to describe systematically or influence usual care (UC) for hypertension. Routine primary care at that time included options for a PCP to order conventional home BP monitoring, no-cost BP check visits with a medical assistant, and referral to MTM pharmacists for in-person consultations without prolonged monitoring or telephone follow-up. There was monthly feedback to physicians regarding BP levels in their patients with diabetes, and promotion of then-current national and regional guidelines for hypertension management , 21.In Hyperlink 3, in recognition of improvements in hypertension care that had been adopted since Hyperlink 1, the comparator was called “best practice clinic-based care.” The best practices were recommended by professional organizations  at the time the study was designed and were affirmed in subsequent national guidelines . New infThe Hyperlink 1 intervention is described in detail elsewhere . BrieflyThe telehealth care intervention in Hyperlink 3 was similar to the Hyperlink 1 intervention, but differed in the following respects . (1) RouAs noted above in the section on recruitment, study resources went into identifying and enrolling eligible participants for both studies. Hyperlink 1 UC and Hyperlink 3 clinic-based care required no additional expertise or resources to deliver the interventions. For the Hyperlink 1 telemonitoring intervention, pharmacists received 8 h of study protocol training plus 1:1 observation of their first study patient visit, their labor was paid for by study funds, and the telemonitoring was also paid for by study funds. For the Hyperlink 3 telehealth care intervention, pharmacists received 3.5 h of study protocol training plus 1:1 observation of their first patient visit, and both their labor and the telemonitoring was paid for by the health system. In addition, study staff attended part of monthly MTM pharmacist staff meetings for ongoing communication about study issues and progress.Hyperlink 1 UC and Hyperlink 3 clinic-based care were very flexible and pragmatic, except that the Hyperlink 3 default initial follow-up recommendation was for a medical assistant BP check. For the Hyperlink 1 telemonitoring intervention, pharmacists were asked to follow a fixed visit schedule and protocol for medication intensification that was based on the existing collaborative practice agreement. For the Hyperlink 3 telehealth care intervention, the pharmacists were allowed more flexibility in the visit schedule, intervention duration, medication intensification approach, and management of conditions in addition to hypertension.Hyperlink 1 UC patients had routine attention to adherence and no additional assistance with scheduling follow-up. For the Hyperlink 1 Telemonitoring Intervention, research staff scheduled enrolled patients for the initial in-person pharmacist visit but phone follow-up visits were scheduled by the pharmacists. The pharmacists encouraged telemonitoring, medication, and phone visit adherence.In Hyperlink 3, any assistance patients in both intervention groups received with scheduling the initial in-person follow-up- visit was from clinic staff according to workflows and capacities that varied by clinic and over time. Enrolled patients in clinics randomized to clinic-based care were given a signed referral order to follow up with a medical assistant for a BP check within 2 weeks, or another type of follow-up if the PCP had changed the default. Patients could schedule their follow-up visit in the clinic or by phone. Clinic assistants used a “referral work queue” to place up to two phone calls to reach non-scheduled patients and then sent a letter to non-responders. Any further follow-up and attention to adherence was at the discretion of the patient and care team, but patients with uncontrolled BP could also be contacted by staff from either the clinic or a centralized resource to schedule BP follow-up through the hypertension registry. Patients in clinics randomized to telehealth care had a signed referral order to follow up with a pharmacist within 2 weeks, or another type of follow-up if the PCP had changed the default. Follow-up scheduling in telehealth care clinics was similar to clinic-based care clinics except that an MTM program coordinator used the referral work queue to schedule the initial in-person pharmacist visit. For patients who attended the initial in-person pharmacist visit, pharmacists encouraged uptake and adherence to telemonitoring, as well as both medication and phone follow-up visit adherence.In Hyperlink 1, research clinic follow-up visits were scheduled at 6, 12, 18, and 54 months following enrollment to measure BP and administer surveys for patient-reported outcomes. In Hyperlink 3, there were no research visits. BP measures were extracted from routine visits in the EHR and patient-reported outcomes were collected in surveys conducted by telephone, mail, or online at baseline, 6, 12, and 24 months.In Hyperlink 1, the primary outcome was BP control at 6 and 12 months as measured in research clinic visits. In Hyperlink 3, the primary outcome was the change in SBP over 12 months of follow-up as measured by BP values that were routinely collected in clinical encounters and extracted from the EHR. In both trials, the difference in the primary outcome between groups was compared. A focus group of patients with hypertension was conducted in preparation for the Hyperlink 3 trial. This group endorsed BP as the primary outcome of importance to them.The primary analysis for both trials was according to randomized group (intention-to-treat). In Hyperlink 3, we added a secondary per-protocol analysis to examine the outcomes in patients who were adherent to the intended interventions in the two study arms. In the clinic-based care group, this was defined as a visit with a medical assistant for a BP check within 6 weeks of enrollment. In the telehealth group, this was defined as a visit with a pharmacist or nurse practitioner within 6 weeks of enrollment, sending at least one home BP measurement, and attending a follow-up phone visit with the pharmacist or nurse practitioner.rather pragmatic in the setting domain, in that the clinics were representative of routine primary care, but in a single well-resourced integrated health system. Hyperlink 1 recruitment was rather explanatory given its reliance on research staff, although some early steps in assessing eligibility were pragmatic in using EHR data to narrow the mailings to patients with elevated BP at recent clinic visits. In contrast, Hyperlink 3 recruitment was very pragmatic in its use of automated EHR algorithms; use of existing staff, tools, and processes to enroll patients and arrange follow-up; and partial waiver of informed consent. Both trials were rather pragmatic in the eligibility domain in attempting to include typical patients with uncontrolled hypertension and exclude relatively few people except those who required special expertise for hypertension care. Hyperlink 3 had fewer exclusions, but it included only patients with moderately severe uncontrolled hypertension.The PRECIS-2 wheels for Hyperlink 1 and 3 are shown in Fig. rather explanatory, while the Hyperlink 3 telehealth care intervention costs were absorbed by the health system, making it rather pragmatic. In the flexibility of delivery domain, the UC and clinic-based care comparators were very pragmatic. The Hyperlink 1 telemonitoring intervention was more prescriptive and thus rather explanatory, while the Hyperlink 3 telehealth care intervention was more tailored to the individual patient and thus rather pragmatic. In the flexibility of adherence to intervention domain, Hyperlink 1 UC and Hyperlink 3 clinic-based care and telehealth care were very pragmatic for adherence to initial follow-up and subsequent care recommendations. The Hyperlink 1 telemonitoring intervention was very explanatory for adherence to initial follow-up, and the pharmacist care was rather explanatory in that it was more prescriptive than their typical hypertension care. In contrast, in Hyperlink 3 telehealth care, adherence to initial follow-up was very pragmatic, while the pharmacist care was rather pragmatic in imposing fewer constraints on their typical hypertension care.In the organization domain, the UC and clinic-based care comparators were very pragmatic. The Hyperlink 1 telemonitoring intervention was paid for by the study, making it very explanatory for Hyperlink 1 and very pragmatic for Hyperlink 3. The primary outcome domain was very pragmatic for Hyperlink 3 based on the patient focus group results and the use of BP measured in routine care as the primary outcome. The primary outcome domain was rather pragmatic in Hyperlink 1 because BP measured in a research clinic is less relevant to patients. The primary intention-to-treat analysis was very pragmatic for both trials.The follow-up domain was In Hyperlink 1, among 15,459 adult patients who were potentially eligible based on age and BP recorded in the EHR and selected for contact by mail, 2020 (13.1%) expressed initial interest in participating and were contacted to be further screened in the research clinic  at the current and previous encounter. Of the 3794 eligible patients, 3071 (81%) had a signed hypertension follow-up order and were enrolled. The proportion of eligible patients enrolled was somewhat higher in the clinic-based care group (85%) than in the telehealth group (77%), and for those with signed follow-up orders, PCPs were less likely to change the default follow-up from the intended provider in the clinic-based care group  than in the telehealth group (75% retained pharmacist).In Hyperlink 3, among 222,133 patients aged 18–85 seen in 21 primary care clinics  over 18 months, 69,480 patients were potentially eligible based on having two or more encounters with a hypertension diagnosis code within the last 24 months ; more likely to be Asian (6.9% vs. 5.5%) or Black (19.3% vs. 14.0%); and less likely to be male (46.6% vs. 51.4%) and White (69.4% vs. 76.5%). Compared with non-enrolled patients, in the enrolled patients, BP was higher (158/92 mm Hg vs. 156/89 mm Hg); and they were more likely to have obesity (57.1% vs. 51.5%), but less likely to have diabetes (25.2% vs. 27.2%) or cardiovascular disease (16.7% vs. 19.2%).Compared to Hyperlink 1, enrolled patients in Hyperlink 3 were about 1 year younger (60.2 vs. 61.1 years), had a lower proportion of male patients (46.6% vs. 55.3%) and White patients (69.4% vs. 81.8%), and had a higher proportion of Asian patients (6.9% vs. 1.6%) and Black patients,  attended the in-person pharmacist visit and 217 (95%) transmitted at least one BP measurement with the telemonitor , a consequence of the semi-automated method of enrollment and the very large number of patients who would have been eligible at the lower BP threshold in Hyperlink 1 (>140/90 mm Hg at 2 consecutive visits). This type of choice is one often faced by both pragmatic trialists and health system leaders faced with limited resources and large populations in need of care. The higher BP for eligibility in Hyperlink 3 may have had some effect on the characteristics of the enrolled population, but it seems likely that the greater inclusiveness of the recruitment method and the waiver of the informed consent process were the primary drivers of the more vulnerable population enrolled in Hyperlink 3 One major departure from Hyperlink 1 was the much higher BP required for Hyperlink 3 eligibility  and the telehealth group (77%), likely because physicians or patients preferred accepting the default follow-up with the more familiar medical assistant. Although the study team had expected moderately lower adherence to the telehealth intervention than the 98% observed in the highly motivated and consented patients in Hyperlink 1, we were surprised by the very low adherence to the hypertension referral follow-up visits in both groups in Hyperlink 3. Hyperlink 3 patients also sent in fewer home BP measurements than Hyperlink 1 patients (mean 32/week vs. 6/week).Although the low-touch recruitment methods in Hyperlink 3 succeeded in enrolling a larger and more representative sample of patients with uncontrolled hypertension without using traditional research methods and staff, these patients were much less likely to adhere to the follow-up visit, and thus less likely to be exposed to the telehealth intervention. Although the PCPs signed the referral order for follow-up, we do not know if they discussed it with the patients or encouraged it. Only routine clinic resources were used for scheduling follow-up. There was no written consent to participate in the study, further reducing patient awareness of the follow-up care recommended for them. The low participation rate we observed is likely to dilute any positive effect of the intervention as assessed by the gold standard of intention-to-treat analysis. In retrospect, we should have anticipated this challenge based on other pragmatic trials that were launched at around the same time –30. HoweThese observations also suggest that in pragmatic trials, the primary intention-to-treat analysis demonstrates the real-world effect of offering the intervention widely when intervention adherence and fidelity are promoted as they would be outside a trial. The analysis may be supplemented by a secondary per-protocol analysis that compares results in patients who adhered to the intervention in both groups, using propensity weights to take into account the post-randomization differences between groups . We haveThe pragmatic design of Hyperlink 3 was successful in enrolling a high proportion of eligible patients and those traditionally under-represented in clinical trials , and demonstrating that this could be done without research staff using an EHR algorithm. However, there were trade-offs with much lower adherence than in Hyperlink 1. The lower adherence is likely to greatly reduce any differential intervention effect on the primary outcome, systolic BP, and other secondary outcomes in the more pragmatic Hyperlink 3 study design but likely more accurately demonstrates real-world outcomes associated with an intervention of this type. It will require careful interpretation and supplementation of the intention-to-treat analysis with a planned per-protocol analysis. Nevertheless, for health care systems interested in implementing similar interventions in real-life settings, Hyperlink 3 provides them with a much better estimate of the uptake and an opportunity to devise additional ways to encourage participation. Moreover, these observations support the hypothesis that to improve care of common chronic diseases a variety of intervention or care improvement models may need to be employed so that patients have more than one pathway to improved care.


Adolescence presents a window of opportunity to establish good nutrition and physical activity behaviours to carry throughout the life course. Adolescents are at risk of developing cardiovascular and other chronic diseases due to poor the complex interplay of physical and mental health lifestyle risk factors. Text messaging is adolescents main form of everyday communication and text message programs offer a potential solution for support and improvement of lifestyle health behaviours. The primary aim of this study is to determine effectiveness of the Health4Me text message program to improve adolescent’s physical activity or nutrition behaviours among adolescents over 6-months, compared to usual care.Health4Me is a virtual, two-arm, single-blind randomised controlled trial, delivering a 6-month healthy lifestyle text message program with optional health counselling. Recruitment will be through digital advertising and primary care services. In total, 330 adolescents will be randomised 1:1 to intervention or control  groups. The intervention group will receive 4–5 text messages per week for 6-months. All text messages have been co-designed with adolescents. Messages promote a healthy lifestyle by providing practical information, health tips, motivation and support for behaviour change for physical activity, nutrition, mental health, body image, popular digital media and climate and planetary health. Virtual assessments will occur at baseline and 6-months assessing physical health , mental health  and lifestyle outcomes .This study will determine the effectiveness of a 6-month healthy lifestyle text message intervention to improve physical activity and nutrition outcomes in adolescents.ACTRN12622000949785, Date registered: 05/07/2022.Australia New Zealand Clinical Trials Registry (ANZCTR)  Adolescence presents a window of opportunity to establish good nutrition and physical activity behaviours, which are the pillars of overall future physical and mental health . HoweverSystematic reviews have shown that poor nutrition, physical inactivity, sedentary behaviour and sleep are all linked to poor mental health –11 and tThe current approach to improving the health behaviours of adolescents needs to be aligned with best practice recommendations . ProgramThe ubiquitous nature of mobile health  programs offers a potential solution to address these risks. There is strong evidence supporting the efficacy of text messaging in adults for facilitating positive behaviour change for physical activity , nutritiThe primary aim of this study is to determine, in a single blind randomized controlled trial (RCT), whether a semi-personalised text message healthy lifestyle program  compared to usual care will improve adolescents’ physical activity  or nutrition (vegetable intake > 3 serves per day) behaviours among adolescents over 6-months. Further, to determine if the text message program can improve or maintain other physical and mental health and lifestyle outcome, to explore the acceptability, utility and engagement with Health4Me among youth and inform implementation into primary health care services in Australia.The Health4Me study is a virtual, randomised, controlled, single-blind trial, which delivers a 6-month text message healthy lifestyle program with optional health counselling to 330 healthy adolescents (12–18 years old inclusive) . Participants will view digital advertisements for the study on social media, after clicking on a link it will take them to the participant information sheet which they can read and provide informed e-consent. After obtaining electronic consent from adolescents (and their parents or guardians if < 14 years old) and completing the baseline assessment on a secure web-based database, a member of the research team will randomize the participant via a centralized, computerized randomisation program in a uniform 1:1 allocation ratio (intervention: control). Randomization is based on both permuted blocks with randomized block sizes and stratification, where the strata are age and gender. A randomization list will be generated by an independent statistician. Members of the research team who enrol and conduct follow-ups with participants will be blinded to group allocation.For each participant, the randomization program automatically produces a study identification number. On the following Monday after the baseline assessment, the computer system automatically sends the assigned text message program to the participant. Therefore, the random allocation sequence will be concealed from study personnel at baseline and at 6-months. Participants will be encouraged not to discuss whether they are or are not receiving the text messages until the end of the 6-month follow-up.As the study is virtual, there are no physical sites for recruitment. Rather, participants will be recruited through digital advertisements. GPs will also be informed of the study via email and encouraged to advertise this to their adolescent patients. A log of all recruitment strategies will be kept. Participants will be eligible to take part if they are: (1) 12–18 years old inclusive; (2) own a mobile phone capable of sending and receiving text messages; (3) have sufficient English to read text messages pitched at a 7th grade reading level; and (4) provide electronic consent (or from their parents or guardians if < 14 years old).Participants will be excluded if they: (1) have a diagnosis of type 1 or type 2 diabetes mellitus; (2) have a previous or current diagnosis of an eating disorder or are at high risk for an eating disorder as assessed in screening; (3) weight < 25th centile for age; (4) have had recent rapid weight loss; (5) have a medical condition which would preclude providing informed consent or ability to comply with the study protocol; (6) are enrolled in an alternative randomised lifestyle management program; (7) are pregnant or planning to become pregnant during the 6-month intervention; and (8) are unable to read English at a 7th grade reading level. Screening will occur for eating disorders at baseline, 2, 4 and 6-months and any participant who scores above the pre-determined threshold will trigger the study team to refer appropriately. Both screening and recruitment logs will be kept by the research team for those participants who are ineligible or decline to participate, including reasons for non-participation.The control group will receive usual care. For this study, usual care is defined as accessing available information and health services to maintain a healthy lifestyle. Participants in the control group receive an initial text message welcoming them to the study, and one text message at 2, 4 and 6 months respectively. The messages at 2 and 4 months will have a link to complete the eating disorder screening and the message at 6 months will direct them to complete all follow-up assessments. Participants in the control group will be offered the text message intervention after the 6-month follow-up assessments are complete at no cost. However, they will not be able to receive health counselling.The intervention group will receive usual care, plus a 6-month text message support program with text messages focussed over six key priority areas for improving physical and mental health, namely nutrition, physical activity, mental health, body image, climate and planetary health and popular digital media , as well as the option to speak with a university-trained health counsellor.Message program content was developed according to a previously published model . BrieflyEach intervention participant will receive a semipersonalized and customized set of text messages, sent on four random days per week and at random times. If the participant is attending high school, the weekday text messages will only be sent before or after school hours (8.00 AM to 9.00 AM or 3.30 PM to 7.30 PM). Each text message is unique and will only be sent once throughout the 6-month program. The messages are semipersonalized by including the participants name and selecting content relevant to their age and dietary behaviours . Participants can also update their key information,  through the course of the study and this will be reflected in the ongoing text messages they receive. Messages are sent at no cost to the participants and a bulk-rate cost to the study. However, if participants reply to the study team, these will be paid for by the participant at standard short message service (SMS) rates. The intervention will encourage two-way communication; however, it is for the participant to decide how much they engage with the study. All replies and responses are monitored by the health counsellor and responses given in 72 hours when required.Once a month , intervention participants will be sent a text message encouraging them to call the university qualified health counsellor to ask questions or request additional information. The personalised health counselling calls will last 10–15 minutes and will be delivered according to standardized protocol. The university qualified health counsellor  will monitor and respond to participants’ request for a call each month either via text message or phone call within 3 working days. Participants are allowed 6 health counselling calls in total over 6-months. The health counselling calls will allow participants to set behavioural goals, discuss barriers and enablers to behaviour change, and their overall progress. This part of the intervention is based on the evidence based TEXTBITES Study for obesity prevention in adolescents .The text message management system has been used successfully in two randomised controlled trials (TEXTBITES and EMPOWER-SMS) , 43. PreData will be collected from participants at baseline and 6-months online. In addition, for safety the eating disorder screening will be conducted at 2-months and 4-months for all participants. The co-primary outcomes, secondary outcomes and their assessments are provided below in Table Weight, height and waist circumference will be self-reported by participants according to a standardised digital protocol . ParticiThe following questionnaire-based assessments have demonstrated reliability and validity in adolescent populations and will be completed online at baseline and follow-up assessments. Sleep quality will be measured using the Pittsburgh Sleep Quality Index-Short , 49. HeaThe acceptability, utility and engagement with the Health4Me Program will be measured through quantitative and qualitative measures. Firstly, text message data will be extracted from the software system to assess engagement by the number of messages sent, number of responses received, and number of messages ‘bounced’ and undelivered. Secondly, a study-specific user feedback and satisfaction questionnaire will be administered to all participants. It will contain questions on a 5-point Likert scale from strongly agree to strongly disagree regarding acceptability and utility of the program. It will also include open ended questions regarding most and least useful program components and suggestions for program improvement. These responses will be coded thematically, and emerging themes identified. The questionnaire will also ask questions about whether participants used or accessed any other digital health tools to help manage their lifestyle health behaviours during the study. Intervention participants will be invited by text message, email, or phone call to join focus groups after the 6-month follow-up. A minimum of five focus groups will be conducted consecutively until thematic saturation is reached. The focus groups will be conducted via Zoom teleconference and last approximately 45 minutes. Participants will be purposively selected to ensure a mix in terms of age, ethnicity and location are represented to encompass different viewpoints.For the outcome of MVPA (minutes per day), 312 (156 per group) participants are needed to achieve 90% power to observe the mean difference of 14.8 (control: 42.55 and intervention: 57.36) with standard deviations (SD) of 21.45 for control and 37.79 for intervention and accounting for 30% dropout. A two-sided two-sample unequal-variance t-test was used. The Bonferroni adjusted significance level of 0.025 was used to account for two primary outcomes. To detect a difference in vegetable intake at 6-months, 390 (195 per group) participants will achieve 90% power to detect 13.37% difference in the proportion of appropriate vegetable consumption (control: 4.85% and intervention: 18.22%) and accounting for 30% dropout . The twoAll statistical analyses will follow a pre-specified statistical analysis plan guided by our team statistician. Analyses of the primary and secondary outcomes will be conducted according to the intention-to-treat principle. Continuous outcomes at 6-months will be analysed using analysis of covariance (ANCOVA), and categorical outcomes at 6-months will be analysed using log-binomial regression, adjusting for the outcome values at baseline. Planned subgroup analyses will investigate interactions between treatment and subgroups, including categories of age, socioeconomic status and ethnicity, to explore trends for scale up. All tests will be two-sided with a significance level of 0.05. Analyses of the two primary outcomes will be controlled for the family-wise error rate using Holm correction . No multPrimary ethics approval has been received from the University of Sydney Human Research Ethics Committee (2022/402). Any modifications to the protocol will be submitted to the ethics committee. E-consent will be collected from all participants (and their parents or guardians if < 14-years-old). The sponsor for the study is the University of Sydney and they have no role in the study design, collection, management, analysis and interpretation of data, the writing of findings or submission of findings for publication. The design and conduct and dissemination of the study will be overseen by the Health4Me steering committee .Current obesity prevention approaches are ineffective and there are limited programs beyond the school setting available which suit the needs of adolescents, coupled with an escalating mental health crisis for young people in Australia. Positively framed prevention programs which focus on good nutrition and physical activity behaviours can address both critical issues and these programs must be accessible for adolescents. This study aims to provide information and support for lifestyle risk factors by developing and testing a 6-month text message primary prevention program for adolescents in an RCT. It is expected that the results will include improvements or maintenance in primary and secondary outcomes. If effective, this study will inform future translational research to improve the physical and mental health of adolescents and prevent the future development of chronic diseases in adulthood and implementing such a program into healthcare services throughout Australia. Results from the process measures will identify barriers and enablers of widespread implementation of the text message program, with the goal of providing accessible and accurate lifestyle health information and support to adolescents to establish lifelong healthy behaviours.This study will test the effectiveness of a 6-month text message intervention to improve and support the physical and mental health outcomes of all adolescents. This study will also provide information on the barriers and enablers of the text message program and ways in which it can be improved. If effective, the results will provide high quality evidence to inform future translational research to scale up the program and embed it within healthcare systems throughout Australia.


Most existing algorithms for this task are designed for offline settings, producing a single reconstruction from a batch of images taken from diverse viewpoints. Alongside reconstruction accuracy, additional considerations arise when 3D reconstructions are used in real-time processing pipelines for applications such as robot navigation or manipulation. In these cases, an accurate 3D reconstruction is already required while the data gathering is still in progress. In this paper, we demonstrate how existing batch-based reconstruction algorithms lead to suboptimal reconstruction quality when used for online, iterative 3D reconstruction and propose appropriate modifications to the existing Pix2Vox++ architecture. When additional viewpoints become available at a high rate, e.g., from a camera mounted on a drone, selecting the most informative viewpoints is important in order to mitigate long term memory loss and to reduce the computational footprint. We present qualitative and quantitative results on the optimal selection of viewpoints and show that state-of-the-art reconstruction quality is already obtained with elementary selection algorithms. Multi-view 3D reconstruction has applications in offline tasks such as medical imaging  and objeReconstruction algorithms have been presented in the literature that operate on various sensor modalities, including LIDAR , RADAR , RGB-D  and monoEarly methods for 3D reconstruction focused on the mathematical and geometric aspects of reconstruction. Viewpoints are interpreted as 2D projections, and the goal is to determine the inverse projection from matching features across viewpoints. To make this feature matching tractable, multiple cameras are needed with largely overlapping viewports and within a well-calibrated distance. With the advent of deep learning, scholars have been able to frame the problem of reconstruction as a recognition problem . Deep neAn extension of the feedforward Pix2Vox+/F architecture  that supQualitative and quantitative results that demonstrate the performance degradation of applying existing reconstruction algorithms in iterative settings both to the R2N2 dataset and to a higher-resolution extend version.A characterization of the variation in reconstruction quality that arises from viewpoint selection.Notably, the earliest DNN architectures for 3D reconstruction used recurrent neural networks (RNNs), for instance, 3D-R2N2  and LSM The remainder of this paper is structured as follows. In the next section, we discuss related work on 3D reconstruction. This is followed by Conventional monocular methods for 3D reconstruction, such as structure from motion (SfM)  and visuMulti-view stereopsis (MVS) refers to 3D reconstruction from calibrated overlapping images. Early data-driven MVS techniques include SurfaceNet ,22, whicOne of the earlier multi-view 3D reconstruction approaches not using camera parameters was 3D-R2N2 . SimilarThe latest models have incorporated an attention-based mechanism to select relevant features from each input view. AttSets  is a CNNRecently, researchers have started exploring neural implicit functions as an effective 3D geometry representation, representing 3D content in a continuous function in contrast with the traditional discretized voxel-based representations. Implicit functions such as neural radiance fields do not require 3D object representations during training and can be trained only on 2D images ,26. NeurIn this section, we first detail the architecture of our 3D multi-view iterative reconstruction model. Secondly we describe the performance metrics used in this paper. Lastly, we describe the original dataset and the extension we applied to create more viewpoints of each object in order to study the effect of viewpoint selection.Our architecture for iterative 3D reconstruction is an adapted version of Pix2Vox++, see The Pix2Vox++/F architecture is structured in three components: encoder, decoder and merger. A batch of 2D images is converted by the encoder into a lower-dimensional feature map. The encoder consists of a backbone model (we tested both VGG and ResNet backbones) followed by three blocks of a 2D convolution layer, 2D batch normalization and ReLU activation. The middle block has an additional 2D maxpool layer. The encoded features are then mapped by the decoder to a 3D voxel representation per input view. The decoder architecture consists of four blocks each having a 3D transposed convolution layer, a 3D batch normalization layer and a ReLU activation. This is followed by a fifth block with a last 3D transposed convolution and a sigmoid activation function.Due to self-occlusion, different viewpoints offer better visibility for certain parts. The goal of the merger is to select the best parts in each of the individual 3D representations produced by the decoder. The merger outputs a score map for each volume generated by the decoder, which are normalized using the softmax function. Its architecture comprises six blocks of a 3D convolutional layer, 3D batch normalization and a leaky ReLU activation. The outputs of the first four layers are concatenated and used as input for the fifth layer. To address the loss of detail in the feature maps of the deepest convolutional layers of the merger, an additional tensor is provided as input to the merger containing the feature maps of the last ReLU activation and 3D transposed convolutional layer of the decoder. This tensor contains nine channels per voxel. The final (single) 3D reconstruction is obtained by calculating a weighted sum of these volumes according to these score maps.In an iterative setting, not all input images are available at once. For the encoder and decoder model, this is not an issue, since their outputs do not depend on other images. The merger, however, as discussed above, uses softmax normalization to normalize the score maps for each image in the batch. This prevents its use in an iterative setting. By keeping a fused context tensor in memory, see Even though we did not alter the architecture of the merger, its input changed, which resulted in a different training regime. The merger now has to learn how to compress all previously seen information into one fused context tensor instead of solely trying to combine the best parts of each reconstruction.The reconstruction model assigns a probability score to each voxel in the 3D representation. When this score exceeds a set threshold value, the voxel is assumed to be occupied. In this work, we used a threshold value of 0.4. This value was determined after an initial hyperparameter search; see A with the set of voxels in the ground truth B. In particular, we used the intersection over union (IoU) ratio:The quality of a single 3D reconstruction was measured by comparing the set of voxels in the reconstructed representation We assume that new 2D viewpoints become available at each discrete time step R2N2  is the mTo allow for an in-depth analysis of viewpoint-selection algorithms, we require more than 24 viewpoints. For this reason, we created additional renderings (2D images) from the original ShapeNet model using the Pytorch3D library . We usedPer object, 120 viewpoints were rendered according to a simulated drone flight path. Subsequent viewpoints have a fixed azimuth difference of To demonstrate the generalizability of the models, we also experimented with the Amazon Berkeley Objects (ABO) dataset . The ABOIn this section, we first benchmark our iterative 3D reconstruction algorithm to existing algorithms in the literature by the IoU metric (Equation ). In SecIn our first experiment, we compared the quality of 3D reconstructions obtained by our iterative approach to the reported performances of other state-of-the-art approaches on the R2N2 dataset, as listed in From these results, we can conclude that our iterative model results in better reconstructions than the RNN-based 3D-R2N2 model, which is to our knowledge the best-performing iterative model ) and its computational requirements. We compare our iterative algorithm to an online setup with the original Pix2Vox++/F model. The latter model can take as input batches of any size, including batch size 1 (single-viewpoint). Using the Pix2Vox++/F model in an online setting can therefore be achieved by buffering the most recent viewpoints, using a first-in-first-out replacement strategy, and using this buffer as batch input to the algorithm.In our initial experiments, we presented a new viewpoint every timestep from the extended dataset, in order of increasing azimuth. For both algorithms, we observed a high fluctuation in reconstruction quality; see To avoid long-term memory loss in the iterative algorithm and ensure sufficient viewpoint diversity in the input batch of Pix2Vox++/F, we therefore conducted our benchmark experiment on a subsampled version of the original R2N2 dataset. After ordering the 24 viewpoints of an object by azimuth, we selected every second image.Limiting the batch size of Pix2Vox++ induces a performance degradation in the reconstruction quality. Only the condition where all viewpoints in the batch are processed every timestep (Growing Buffer) results in a performance that is on-par with our algorithm. This setting, however, in addition to being slow, also requires keeping 12 images in memory, which results in additional memory resources when compared to the fused-context tensor from the iterative approach.To quantify the speed of both reconstruction models, we testde them on various hardware setups, as shown in k viewpoints is selected from the n images available. Since selecting k elements out of n scales exponentially in n, we only present quantative results obtained from testing all possible combinations of the smaller R2N2 dataset  can be obtained in the initial phase of sequential reconstruction. We implemented this method using the following equation:•MSE difference calculates the mean-squared error (MSE) (pixel-wise difference) between the current viewpoint and the previously selected viewpoint. This MSE is then multiplied by an arbitrary value; the resulting value is then the chance of selecting that viewpoint. This strategy was chosen in favor of handpicking a specific threshold because for different objects, the baseline MSE between consecutive viewpoints can vary substantially. The arbitrary value was then chosen such that for most objects, In this paper, we studied the problem of monocular, iterative, multi-view 3D reconstruction. Compared to the traditional batch-based 3D reconstruction, iterative reconstruction entails novel challenges related to viewpoint selection for maximizing IoU over time with the minimum number of images possible. We showed that existing batch-based 3D reconstruction methods, such as Pix2Vox++, can be modified to work in an iterative setting. Our method, based on iteratively updating both the context vector and the reconstruction, outperforms repeated batch-based processing, since it counteracts possible long-term memory loss while being much more computationally efficient. We qualitatively and quantitatively studied the effects of viewpoint diversity and selection on reconstruction quality. Results indicate distinct intervals with less and more informative viewpoints, but these intervals are highly object-specific. This makes creating a more intelligent selection system infeasible with the current state of 3D deep learning models. We can thus conclude that periodic selection of viewpoints generally provides the best results in terms of in-flight and final reconstruction quality. In future work, we will investigate if this object specificity is related to the low resolution of the 3D reconstruction. We will research deep learning approaches that can produce higher-resolution 3D reconstructions without increasing computational requirements.