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Identification and Treatment of Underlying Cause by Case Variant | DISEASE PROGRESSION | We next disaggregated the combined DxTx score to explore how well physicians identified and managed the root problems of their patient’s symptoms (medication nonadherence, DDI, or disease progression). | PMC10105436 | |
Medication nonadherence | nonadherence | REGRESSION | At baseline, among the NA patient cases, providers identified NA in their patients only 2.0% of the time, with no difference between study arms (p = 0.414) (Table
Primary Variant Diagnosis and Related Treatment, by Case VariantThe improvement in NA case diagnosis in intervention 1 led to improved clinical care manifested by continuing medication and discussing the importance of medication adherence. After introduction of the CDM test, intervention continued the nonadherence medications by an additional 20.8% (p = 0.003) (Table Regression modeling confirmed that intervention were 50.4x more likely to identify NA (95% C.I. 2.9-871.2) and 3.3x more likely to continue the medications for which their patients were nonadherent, although the latter proved not to be significant (O.R. 0.6–19.3). | PMC10105436 |
Drug-drug interactions | DDIs | At baseline, identification of DDIs was modest in both control and intervention (7.8% for both arms, p = 0.532). After introduction of the CDM test, intervention significantly improved their ability to identify DDIs, increasing from 6.5 to 57.1% (p < 0.001) compared to contro which did not change (9.2–9.3%, p = 0.979).After identifying more DDIs, intervention was nearly twice as likely to make a clinical adjustment by typically either stopping the interacting medications or shifting to a different medication (32.5–64.9%, p < 0.001) compared to control (32.9–12.0%, p = 0.002).Here, the fixed effects model confirmed that intervention was 26.9x more likely to identify the DDI (95% C.I. 5.6-130.6) and 15.7x more likely to stop the interacting substance (95% C.I. 5.0–49.0). | PMC10105436 | |
Disease progression | DISEASE PROGRESSION | Although making the diagnosis of disease progression trended in the right direction for these patient cases at + 3.2%— this trend did not reach statistical significance (p = 0.838) and, in the difference-in-difference estimation, the intervention group was 0.3x as likely to diagnose disease progression and 0.3x as likely to advance the medication regimen or increase medication dose. | PMC10105436 | |
Intervention 2 Results | nonadherence, DDIs | REGRESSION | We wanted to determine whether the educational materials increased awareness of nonadherence or DDIs and if so, how this impacted practice and test ordering by physicians.Overall, intervention 2 only ordered the CDM test in 12.4% of patient cases with no significant difference by case variant (p = 0.892). When given the option of ordering the CDM test after reviewing the education materials, they had a nonsignificant improvement in their DxTx domain scores for NA cases of + 2.0%, (p = 0.542) and no improvement in the other case variants when compared to baseline scores. When we looked at diagnosing NA, the second intervention improved from 4.8 to 14.8% (p = 0.031), while the control remained the same (p = 0.989). Intervention 2 also improved from 9.6 to 18.5% in diagnosing DDIs (p = 0.102). When we controlled for physician and practice characteristics, we found a 3.6 × (95% C.I. 0.3–34.8) improvement by intervention 2 to identify nonadherence in the NA patient cases and 1.9 × (95% C.I. 0.4–9.8) improvement in treatment compared to controls. Similarly, for the DDI cases, intervention 2 was 2.3 × (95% C.I. 0.5–9.4) more likely to identify the patient’s DDI and 1.5 × (95% C.I. 0.5–4.4) more likely to treat it.We split intervention 2 into two subgroups and analyzed those who chose to order the CDM test (Int2A) and those who did not order the test (Int2B). At baseline, there was no difference between the two subgroups. However, after introducing the education materials, Int2A were significantly better in DxTx (43.2%±24.8% vs. 21.2%±14.0%), making the appropriate primary diagnosis (60.0% vs. 18.9%, p < 0.001), and in discontinuing the offending agent(s) (50.0% vs. 18.2%, p < 0.001) compared to Int2B.Int2A had similar scores to intervention 1, and Int2B scored similarly to control (Table
Intervention 2 Comparison by CDM Usage Versus Control, Multivariate Regression Modeling* p < 0.05** p < 0.01*** p < 0.001 | PMC10105436 |
Economic Changes in Diagnostic Ordering | When we examined the economic impact of the CDM test, we found that intervention 1 physicians ordered 0.3 fewer low-value tests per case (95% C.I. 0.0 to 0.6). This decrease in test ordering translates to a per case savings of $119 (95% C.I. $20 to $217). | PMC10105436 | ||
Discussion | nonadherence, cardiometabolic disease, DM/HTN, DDIs | DISEASE PROGRESSION, DISEASE, DISEASES | In patients with chronic cardiometabolic diseases, healthcare outcomes depend upon correct diagnoses and effective treatment regimens [We conducted a RCT to determine if the test improved the recognition, diagnosis, and medication management of medication nonadherence and DDIs in patients with chronic cardiometabolic diseases. The results showed large differences between the intervention and the control groups: physicians who used the CDM test were 50.4x more likely to diagnose medication nonadherence and 26.9x more likely to diagnose DDIs compared to the control. Importantly, they also provided improved subsequent care: they were 3.3x more likely to restart the medication, the appropriate way to address nonadherence, and 15.7x more likely to stop or switch the interacting medications, the appropriate treatment for DDIs. The difference-in-difference calculations for DxTx scores, our combined measure of diagnostic and therapeutic improvement, confirmed this effect.Although the CDM test could not explicitly test for disease progression, physicians could diagnose disease progression by deduction after the CDM test excluded NA and DDIs. Even though intervention physicians were more likely to identify disease progression, there was not a significant difference when compared to control in diagnosis or treating disease progression. Should future study confirm our findings that routine use of tools, such as the CDM test, can objectively exclude non-adherence and DDI, a clinician’s ability to determine whether worsening symptoms are due to worsening clinical conditions, ineffective medications, or misdiagnosis could be improved .Interestingly, when we compared overall scores between the three case types, Afib, HF, and DM/HTN, we found no overall differences in diagnosis and treatment between each of the case types in aggregate. We interpret this finding as an indicator of the overriding challenge physicians face when caring for patients with multiple chronic conditions and accompanying polypharmacy, regardless of the specific disease, indicating the CDM Test has value across all four disease states.Overall, only 1 in 8 providers in the elective intervention group chose to order the CDM test, suggesting that even with education, a more compelling narrative on these challenges than presented in our education materials is needed to alert physicians. Notwithstanding, Int2A were significantly more likely to make the primary diagnosis (58.1% vs. 16.5%) and order the correct related treatment (48.4% vs. 22.1%), compared to Int2B. These results closely mirrored the results we saw in the first intervention, who were all given the result, and control who were not.The potential economic impact of the CDM test is compelling. Intervention ordered 0.3 fewer low-value diagnostic tests per case, leading to savings of $119. While savings realized through reduced utilization of low-value diagnostic testing is in line with the potential the CDM test costs, our analysis does not include larger direct costs; factoring in lower clinical, emergency, and hospital visits, the direct cost benefits would increase significantly. NA is estimated to cause 150,000 emergency room (ER) visits and over one million hospitalizations per year. DDIs, similarly, have been associated with 74,000 annual ER visits and 195,000 annual hospitalizations [Our findings have important implications for patients [While we made a careful effort to present cases of chronic cardiometabolic conditions commonly encountered in primary care, the nine cases used in this study could not cover all possible presentations of medication adherence and DDIs. The CDM test does not test for every drug combination; and major drug groups used by these patients were not tested for, such as, parenteral medications (including insulin), but the test incorporates a significant number of commonly encountered prescription and non-prescription substances capable of contributing to pharmacokinetic or pharmacodynamic interactions when taken with prescription medications used to treat cardiometabolic disease. This study also did not collect patient-level data, and although CPV simulations have been validated against actual practice in numerous studies, future research can address this limitation [ | PMC10105436 |
Conclusion | cardiometabolic disease | Medication nonadherence and DDI are preventable sources of patient harm and poor health outcomes in chronic cardiometabolic disease management. Improved diagnosis using a reliable and convenient test potentially improves patient quality of life, medication safety, clinical outcomes, and cost-efficient health delivery. | PMC10105436 | |
Acknowledgements | None. | PMC10105436 | ||
Author contributions | Conception: JWP, JS, RH; Design: JWP, CV, CW, JS; Supervision: CV, DG; Data Collection and Validation: CV, RH; Formal Analysis and Interpretation of Data: DP, JWP; Drafting the manuscript: DG, CV, DP, JWP; Critical Revisions of the Manuscript: All authors; Final Approval of the Manuscript: All authors. | PMC10105436 | ||
Funding | This study was funded by Aegis Sciences Corporation, Nashville, Tennessee, USA (Grant #N/A). | PMC10105436 | ||
Data Availability | Data supporting these results available on reasonable request to the corresponding author. | PMC10105436 | ||
Declarations | PMC10105436 | |||
Ethical approval and Consent to Participate | This study was conducted in accordance with ethical standards, approved by the Advarra Institutional Review Board, Columbia, MD, USA, and listed in clinicaltrials.gov (NCT05192590). Voluntary, informed consent was obtained from all participants. | PMC10105436 | ||
Consent to Publish | Not applicable. | PMC10105436 | ||
Competing Interests | CPVs®, QURE Healthcare’s proprietary simulated case tool, were used to collect data and score the responses. Three authors (JS, CW, and RH) are employees of Aegis Sciences Corporation, who funded the study. Otherwise, there are no disclosures. | PMC10105436 | ||
List of abbreviations | DISEASE | atrial fibrillationadherent, no drug-drug interactionChronic Disease ManagementClinical Performance and Valuedrug-drug interactiondiabetes mellitusdiagnosis-treatmentheart failurehypertensionnon-adherenceodds ratioprimary care physicianrandomized controlled trial | PMC10105436 | |
References | PMC10105436 | |||
Abstract | PMC10323314 | |||
Introduction | Person‐centred HIV | Person‐centred HIV prevention delivery models that offer structured choices in product, testing and visit location may increase coverage. However, data are lacking on the actual uptake of choices among persons at risk of HIV in southern Africa. In an ongoing randomized study (SEARCH; NCT04810650) in rural East Africa, we evaluated the uptake of choices made when offered in a person‐centred, dynamic choice model for HIV prevention. | PMC10323314 | |
Methods | Using the PRECEDE framework, we developed a persont‐centred, Dynamic Choice HIV Prevention (DCP) intervention for persons at risk of HIV in three settings in rural Kenya and Uganda: antenatal clinic (ANC), outpatient department (OPD) and in the community. Components include: provider training on product choice (predisposing); flexibility and responsiveness to client desires and choices (pre‐exposure prophylaxis [PrEP]/post‐exposure prophylaxis [PEP], clinic vs. off‐site visits and self‐ or clinician‐based HIV testing) (enabling); and client and staff feedback (reinforcing). All clients received a structured assessment of barriers with personalized plans to address them, mobile phone access to clinicians (24 hours/7 days/week) and integrated reproductive health services. In this interim analysis, we describe the uptake of choices of product, location and testing during the first 24 weeks of follow‐up (April 2021−March 2022). | PMC10323314 | ||
Results | A total of 612 (203 ANC, 197 OPD and 212 community) participants were randomized to the person‐centred DCP intervention. We delivered the DCP intervention in all three settings with diverse populations: ANC: 39% pregnant; median age: 24 years; OPD: 39% male, median age 27 years; and community: 42% male, median age: 29 years. Baseline choice of PrEP was highest in ANC (98%) vs. OPD (84%) and community (40%); whereas the proportion of adults selecting PEP was higher in the community (46%) vs. OPD (8%) and ANC (1%). Personal preference for off‐site visits increased over time (65% at week 24 vs. 35% at baseline). Interest in alternative HIV testing modalities grew over time (38% baseline self‐testing vs. 58% at week 24). | PMC10323314 | ||
Conclusions | A person‐centred model incorporating structured choice in biomedical prevention and care delivery options in settings with demographically diverse groups, in rural Kenya and Uganda, was responsive to varying personal preferences over time in HIV prevention programmes. | PMC10323314 | ||
INTRODUCTION | Despite a significant reduction in the number of new HIV acquisitions globally, progress has slowed significantly with a drop of only 3.6% in 2021 compared to 2020 [Further, effective integration of prevention options in HIV prevention delivery models requires understanding how to effectively embed choices within person‐centred care. To address these gaps, we developed a Dynamic Choice HIV Prevention (DCP) delivery model that offers structured choices in product, HIV test modality and location of service delivery, together with patient‐centred staffing, service provision and client support. Within one arm of the study, the intervention arm, we evaluated the uptake of a person‐centred, DCP model among persons at risk of HIV identified at antenatal clinics (ANC), outpatient departments (OPD) and in the community in rural Uganda and Kenya (SEARCH: NCT04810650). | PMC10323314 | ||
METHODS | PMC10323314 | |||
Study setting, design and population | SEXUALLY TRANSMITTED INFECTION | The study population includes persons randomized to the intervention arms of three ongoing pilot trials to evaluate the effect of DCP intervention versus the standard of care. The studies are being conducted in some of the highest seroprevalence areas in rural Southwestern Uganda and Western Kenya [The inclusion criteria for the ANC, OPD and community trials were the same: HIV‐negative status, age 15 years or more and current or anticipated HIV risk. Baseline HIV risk was assessed by asking potential participants if they were at risk for HIV using the country Ministry of Health PrEP screening tool and self‐assessment. The Ministry of Health screener was country‐specific and included questions about having a partner with HIV, diagnosis of a sexually transmitted infection, repeated use of PEP and sex in exchange for money ( | PMC10323314 | |
Study intervention | HIV rapid blood test | The person‐centred “Dynamic Choice HIV Prevention” (DCP) implementation strategy for delivering existing evidence‐based, biomedical prevention interventions was developed using the PRECEDE framework for health promotion strategies to address “predisposing” factors (i.e. knowledge, attitudes or beliefs) that impact behaviour, “enabling” factors to facilitate behaviour and “reinforcing” factors that include consequences of following a behaviour (Table Person‐centred, Dynamic Choice Prevention (DCP) delivery model.
Abbreviations: ANC, antenatal clinics; OPD, outpatient departments.The intervention is being delivered using a person‐centred approach designed to be sensitive and responsive to the choice and preference of the clients. The intervention is being delivered by clinical officers and nurses in the ANC and OPD and by community health workers (CHWs) who facilitate intervention by clinical officers from the local health centre in the community trial. All clinical and community health team staff (i.e. clinical officers, nurses, coordinators and health workers) are trained and equipped for HIV prevention care in the clinical setting, appropriate to their role. Service delivery is deliberately designed to be offered in a warm and friendly atmosphere aimed at making clients feel comfortable during the participant—provider interactions. The intervention is designed to enhance flexibility and convenience by presenting choice to participants with the following components:
Biomedical product choice: the option of oral PrEP or PEP.Service location choice: the options of the location of service delivery, including home, clinic, other community locations and phone/virtual visits.Testing choice: the options of HIV rapid blood test and oral‐based self‐testing (HIVST) with clinician‐assisted testing in cases where participants need help during self‐testing.Refill duration choice: the option to select the duration of their refill (1−3 months) based on their personal preference which hinges on factors, such as travel. | PMC10323314 | |
Measures | Demographics and self‐reported use of any PrEP or PEP in the prior 6 months were collected by survey at the study baseline. At intervention visits weeks 4, 12 and 24, participant selection of structured choice of prevention option (PrEP, PEP, condoms only and no selection), HIV testing modality (oral self‐test or clinician administered rapid antibody) and preferred location for next visit (clinic vs. out‐of‐facility) was recorded. At week 24, PrEP and PEP use and HIV risk (report of sexual partners with HIV or unknown status and/or self‐identification as being at risk) for each of the prior 6 calendar months were assessed via a structured survey. Enrolment began in April 2021, and the data collection for week 24 concluded in March 2022. | PMC10323314 | ||
Analysis | Visit attendance was assessed at weeks 4, 12 and 24 among participants enrolled in the three trials. We excluded all participants who seroconverted and withdrew from the trial. We evaluated the proportion of participants selecting each DCP option at each scheduled visit, and the proportion of participants who ever selected PrEP and PEP during 24‐week follow‐up at each of the three settings. The proportion of follow‐up time covered by biomedical prevention (“biomedical covered time”) for a given participant was calculated as the number of months during which a participant reported PrEP or PEP use divided by the number of months for which self‐reported use was assessed. Participants who acquired HIV were assumed not to be covered during the period prior to seroconversion. “At risk” biomedical covered time was calculated analogously, but restricted to months for which a participant reported HIV risk. We report mean, median, first quartile (Q1) and third quartile (Q3) of both measures across participants. | PMC10323314 | ||
Ethical considerations | Ethical approval to conduct the study was received from the University of California, San Francisco Committee on Human Research (UCSF—Sept 2020), Makerere University School of Medicine Research and Ethics Committee (SOMREC—March 2021), Uganda National Institute of Science and Technology (UNCST—April 2021) and the Scientific Ethical Review Unit of the Kenya Medical Research Institute (KEMRI—April 2021). All participants involved provided written consent to participate in the study. | PMC10323314 | ||
RESULTS | PMC10323314 | |||
Study population | A total of 612 (203 ANC, 197 OPD and 212 community) participants were randomized to the person‐centred prevention intervention (Table Baseline characteristics of 612 participants enrolled in the person‐centred Dynamic HIV Choice Prevention (DCP) intervention in three trials: antenatal clinic (ANC), outpatient department (OPD) and the community.Missing occupation for three participants, marital status for two participants, mobility (nights away) for 26 participants and pregnancy (among women) for seven participants.Abbreviations: PrEP, pre‐exposure prophylaxis; PEP, post exposure prophylaxis. | PMC10323314 | ||
Visit adherence | Between baseline and week 24, 202/203 (99.5%) of participants in ANC, 192/197 (97.5%) in OPD and 210/212 (99.1%) in community settings remained eligible for intervention delivery (four withdrew and four seroconverted; zero died). At week 4 following randomization, 84% of ANC, 89% of OPD and 98% of eligible community participants were seen and offered a dynamic choice of product, test modality and location for the next visit. Visit adherence remained high across all trial settings at weeks 12 (95% ANC, 92% OPD and 91% community participants seen) and 24 (92% ANC, 89% OPD and 89% community). | PMC10323314 | ||
Selections among dynamic prevention choices over time | HIV self‐testing | At baseline, PrEP was selected as an initial prevention product by 98% of participants in ANC, 84% of participants in OPD and 40% of participants in the community (Figure Choice of prevention options: (PrEP—pre‐exposure prophylaxis; PEP—post exposure prophylaxis, condoms or nothing) with each bar representing choices among participants seen at baseline, week 4 (W4), week 12 (W12) and week 24 (W24) in the ANC (left), OPD (middle) and community (right) settings. The different colours represent the different preferences and choice of prevention option. Abbreviations: ANC, antenatal clinics; OPD, outpatient departments.Participants from the three study settings differed in preference for visit location; off‐site delivery of prevention services was initially selected by 93% of community participants, compared to 22% of ANC and 8% of OPD participants. Personal preference for off‐site visits remained high in the community setting (99% at week 24) and increased over time in ANC and OPD (with 51% in ANC and 36% in OPD opting for off‐site delivery at week 24). Across the trials, the most common choice for off‐site visits was homes (86%), followed by phone/virtual visits (7%), trading centres (2%) and schools (2%).At baseline, HIV self‐testing was selected by 34% of ANC participants, 26% of OPD participants and 52% of community participants. In all three settings, personal/individual interest in alternative HIV testing modalities increased over time (57% ANC, 52% OPD and 65% community at week 24). | PMC10323314 | |
Biomedical covered time and dynamic risk | At week 24, the structured survey to assess the use of PrEP or PEP and HIV risk over the prior 6 months was completed by 91% (554/612) participants overall: 94% ANC participants, 87% OPD participants and 90% community participants. Mean biomedical covered time (proportion of 24‐week follow‐up during which a participant reported the use of either PrEP or PEP) was 80% in ANC (median 100%, Q1: 67%, Q3 100%), 60% in OPD (median 67%, Q1 33%, Q3 100%) and 32% in the community setting (median 0%, Q1 0%, Q3 67%). While all participants reported current or anticipated HIV risk at baseline, self‐reported HIV risk experienced, assessed retrospectively at week 24, varied over time (Figure Heat maps of use of biomedical prevention by HIV risk over the 24‐week follow‐up period in the ANC (top), OPD (middle) and community (bottom) settings. Each row corresponds to a participant, and each column to a follow‐up month. Green represents HIV risk with biomedical coverage (i.e. use of oral PrEP or PEP); red represents HIV risk without biomedical coverage; yellow represents no HIV risk but with biomedical coverage; and blue represents no HIV risk and no coverage. Abbreviations: ANC, antenatal clinics; OPD, outpatient departments. | PMC10323314 | ||
DISCUSSION | We implemented a person‐centred model for dynamic choice in HIV biomedical prevention in three distinct settings with demographically diverse groups and found that uptake of intervention components, including product, product delivery and HIV testing modality, varied between locations and over time. This model was responsive to client preferences and resulted in higher retention in prevention services than has been observed in previous studies conducted among subgroups of high acquisition risk [We observed the highest uptake of biomedical prevention among women receiving services at ANC. Reflecting ongoing HIV risk, PrEP was the preferred option for nearly all women. As has been reported by others, PrEP use waned over time [Like in the ANC setting, persons in the OPD setting also preferred oral PrEP with a small proportion opting for PEP as the prevention option of choice at subsequent study visits. Surprisingly, unlike the ANC that has inbuilt retention mechanisms for subsequent pregnancy‐related follow‐up visits, we still observed a high proportion of participants accessing prevention at the OPD clinic setting, which may have been as a result of the patient‐centred care delivery model. Participants also increasingly opted for out‐of‐facility delivery over time, possibly allowing for retention of those who would potentially have dropped off from care if service access was restricted to the clinic. As observed in the ANC clinic, the proportion of participants using the self‐testing option increased with time, enhancing convenience and engagement in continued access to prevention services. We speculate that our uptake and retention was high as compared to other PrEP studies because we offered PrEP in HIV‐status‐neutral settings such as OPD and ANC as opposed to the standard practice of offering PrEP at the HIV clinic in these rural settings, a practice that is associated with increased stigma towards PrEP acknowledging that fear or worry of stigma have been expressed as motivations not to use PrEP [In the community setting, overall uptake of biomedical prevention was much lower than in the two clinic‐based settings. Unlike studies conducted using a community mobile clinic or at community locations besides the household that reported high acceptability [Our dynamic choice model included options for product, testing and delivery on the background of supportive patient‐centred services. Training of providers and CHWs on offering choices without imposing their own views on what might be best for the client was an important part of the intervention. This training included not only the principles of choice but also case studies to illustrate how providers can support the agency for client decision‐making. The training emphasized the delivery of warm patient‐friendly services to foster provider−client trust in discussing HIV risk and the best available option without fear of feeling judged. All providers were trained on patient‐centred delivery prior to the baseline visit. There were monthly meetings of providers, as well as scheduled on‐job booster trainings during the course of the study.Our dynamic choice model increased biomedical covered time during self‐reported HIV risk, but fell short of optimal coverage. The opportunity to add novel, emerging biomedical prevention products such as CAB‐LA as one of the choices for prevention holds promise to increase HIV prevention covered time with this option that has been shown to have higher efficacy than oral PrEP and an ability to confer protection over an 8‐week period following a single administration [Our study has a number of strengths and weaknesses. It is among the first to provide evidence from the real world on biomedical choices selected when offered in different contexts (in contrast to theoretical choices via DCEs). Moreover, this study provides evidence of the implementation of PrEP and PEP in ANC, in OPD clinics (primary care settings) and in the community through a CHW‐led model in regions with high HIV prevalence. It presented an opportunity to explore innovative delivery approaches and demonstrate the value of choice in HIV prevention. Limitations of this study include the short duration of follow‐up and reliance on self‐report. In other words, recall bias is a potential concern, which we aimed to minimize by including prompts in our surveys and limiting them to discrete periods (i.e. months). Additionally, the ongoing trial is confirming that clients were actually ingesting PrEP and PEP with objective biomarkers. In this interim analysis, we are able to show that prevention coverage increased from baseline over 24 weeks among intervention participants, but the comparison to a contemporary control population is lacking in this analysis. Upon each trial's completion, we will compare biomedical covered time, overall and during periods of risk, by the randomized arm; this will help quantify the effect of this model on uptake and retention over a longer duration. These results combined with ongoing qualitative studies of provider and client attitudes can shed light on contributions of various elements of our intervention. | PMC10323314 | ||
CONCLUSIONS | This is one of the first studies to systematically offer a structured intervention for biomedical prevention options using a theory‐based, person‐centred dynamic choice model that adapted services based on client risk and life circumstances over time. This interim analysis demonstrated the intervention was successfully delivered in a variety of settings that are entry points for HIV prevention and can be adapted as new prevention options such as CAB LA become available. | PMC10323314 | ||
COMPETING INTERESTS | The authors report no competing interests in this work. | PMC10323314 | ||
AUTHORS’ CONTRIBUTIONS | LBB | JK, EK, CAK, MN and JA contributed to the study design, data analysis and interpretation, literature search and writing of the manuscript. LBB and MLP contributed to the study design, data analysis and interpretation, literature search and writing of the manuscript. MRK, GC and DVH contributed to the study design, data interpretation and writing of the manuscript. EAB and CSC contributed to the interpretation of the data and writing of the manuscript. All authors have read and approved the final version. | PMC10323314 | |
FUNDING | INFECTIOUS DISEASES, LUNG, ALLERGY, BLOOD, HEART | Research reported in this manuscript was supported by the U.S. National Institute of Allergy and Infectious Diseases (NIAID), the National Heart, Lung, and Blood Institute (NHLBI) and the National Institute of Mental Health (NIMH) and co‐funded under award number U01AI150510. | PMC10323314 | |
DISCLAIMER | The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH. | PMC10323314 | ||
Supporting information |
Click here for additional data file.
Click here for additional data file. | PMC10323314 | ||
ACKNOWLEDGEMENTS | We thank the Ministry of Health of Uganda and Kenya; our research and administrative teams in the United States, Uganda and Kenya; collaborators and advisory boards; and especially all the communities and participants involved. We acknowledge funding support from the National Institute of Health Grant# 1U01AI150510‐01A1 (Havlir). | PMC10323314 | ||
DATA AVAILABILITY STATEMENT | The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions. | PMC10323314 | ||
REFERENCES | PMC10323314 | |||
Background | diabetes | MORBID OBESITY, CHRONIC DISEASES, DIABETES | Health promotion programs are most beneficial in chronic diseases such as diabetes and morbid obesity, which can be positively affected by changes in attitudes, beliefs, and lifestyle. | PMC10069363 |
Objectives | This study aimed to develop an internet-based modern Health Promotion model using interactive online applications through continuing education and participation. | PMC10069363 | ||
Methods | obesity, depression, anxiety, diabetes | OBESITY, TYPE 2 DIABETES, DIABETES | The goal was to positively impact knowledge, behavior, and quality of life for patients with obesity and/or diabetes. This is a prospective interventional study on patients with obesity or type 2 diabetes. Seventeen two patients who met the inclusion criteria were distributed randomly into two groups (control and intervention) from 2019 to 2021 in Greece. All the participants were given questionaries concerning quality of life anxiety and depression (HADS) attitudes and beliefs, knowledge about their condition and general questions to establish a baseline. A traditional health promotion model was followed for the control group. For participants in the intervention group, a web-based health promotion program was created according to the goals of the research. Participants were instructed to log on 1–2 times a week for 5–15 min, with the understanding that the research team would be monitoring their activities. The website included two knowledge games and personalized educational material based on their needs. | PMC10069363 |
Results | obesity, Anxiety, diabetes | OBESITY, STILL, DIABETES | The sample comprised 72 patients (36 in control and 36 in the intervention groups). The mean age was 47.8 years for the control group and 42.7 years for the intervention group (p = 0.293). Both study groups had a significant increase in knowledge score on diabetes (Control group:3,24, Intervention group 11,88 p < 0,001) and obesity (Control group:4,9, Intervention group 51,63 p < 0,001) along with a positive attitude score towards fighting obesity (Control group: 1,8, Intervention group 13,6 p < 0,001). Still, the overall change was more remarkable for the intervention group, as indicated by the significant interaction effect of the analysis. Anxiety was decreased only in the intervention group (Control group:0,11, Intervention group − 0,17 p < 0,005). Analysis for QOL during follow-up showed that Physical Health and Level of Independence was improved in both study groups but the degree of improvement was more significant in the intervention group (Control group 0,31,Intervention group 0,73 p < 0,001). Psychological Health was improved only in the intervention group, with better scores at 6 and 12 months compared to controls (Control group 0,28,Intervention group 1,42 p < 0,001). Furthermore, Social relationships were improved only in the intervention group (Control group 0,02, Intervention group 0,56 p < 0,001). | PMC10069363 |
Conclusions | depression, anxiety | DISEASE, CHRONIC ILLNESS | The results of the present study showed that the participants in the intervention group showed significant improvement in knowledge, attitudes, and beliefs after using the internet as a learning tool. The intervention group also showed significantly reduced anxiety and depression arising from chronic illness. All of this resulted in an improved quality of life regarding physical Health, mental Health, and social relationships. Technology and online-based health promotion programs can revolutionize how we approach the prevention and management of chronic and terminal illnesses by improving accessibility, personalizing care, increasing engagement and motivation, improving data analysis, and disease management. | PMC10069363 |
Keywords | PMC10069363 | |||
Introduction | obesity, death, Diabetes, diabetes | OBESITY, DIABETES, DISEASE, METABOLIC DISORDERS, COMPLICATION, INSULIN RESISTANCE, DIABETES TYPE 2, TYPE 2 DIABETES, DISEASES, DIABETES | The development of the World Wide Web and the Information and Communication Technologies in the 1990s helped spread the Internet from one side of the globe to the other. This enabled people to develop applications, to directly access a wide range of information, and to communicate more easily [It is a fact that the number of new users is constantly growing. In fact, in 2020, perhaps due to the pandemic, there was a 60% increase in the global digital population. In Greece in the same year, there was a 73% increase in internet home access [Smart devices (smartphones and tablets) provide easy and daily access to information, communication and opportunities for motivation. Social networking platforms have invaded the everyday life of users with more than 3 billion monthly active users. And with percentages that still reach up to 70% of adults using at least one platform for 2 h daily [Thousands of health-related websites and applications have been developed, enabling new, friendly health education programs to be implemented [According to the World Health Organization (WHO), trends on both diabetes and obesity are not encouraging. Diabetes is considered one of the top ten causes of death. As defined by the American Diabetes Association, diabetes type 2 is a chronic medical condition characterized by high glucose levels due to insulin resistance and/or inadequate insulin production by the pancreas. It is the most common form of diabetes, and typically develops in adulthood [As far as obesity is concerned, as defined by WHO is an excessive accumulation of body fat, leading to a significant increase in body weight that may affect an individual’s health. The World Health Organization (WHO) defines obesity as a body mass index (BMI) of 30 or higher [The high prevalence of these metabolic disorders, their impact on public health, and the increasingly rising social and economic cost are pushing us towards finding innovative interventions through the internet, guided by the therapeutic team (doctor, dietitian, psychologist, nurse, psychiatrist). The aim of this study was to develop a modern Health Promotion model, through continuing education and participation using interactive online applications that follow the modern technology, is specifically designed to respond individually and personalized to the educational needs of each patient regardless of the education level, since the “wall” of each patient was unique and created according to their needs. The goal was to positively impact knowledge, behavior and eventually quality of life for those who suffer from obesity or diabetes type2. It has been proven that education for patients with type 2 diabetes and obesity is positively related to their adherence to treatment. In addition, providing education on disease management, nutrition, physical exercise, and complication prevention can help patients change their lifestyles and improve their health.Furthermore, the increase in rates of type 2 diabetes and obesity in Greece and worldwide is a serious public health issue. Therefore, a health education program that focuses on preventing and managing these diseases can help reduce their impact on population health and improve the quality of life for patients. Developing health education programs for patients with type 2 diabetes and obesity is essential to improving public health services. | PMC10069363 |
Methods | obesity, cancer, renal failure, diabetes | OBESITY, CANCER, RENAL FAILURE, TYPE 2 DIABETES, DISEASES, DIABETES | This is a prospective interventional study on patients with obesity or type 2 diabetes age group of 18–60 years old, where it was conducted during 2019–2020 in Endocrine Unit, Second Propaedeutic Department of Internal Medicine, Attikon University Hospital, National and Kapodistrian University of Athens, Athens, Greece.Patients were recruited at the Endocrine Unit of the Ddepartment. All the patients who met the eligibility criteria were distributed randomly into 2 groups (control and intervention). Possession of a smart phone and internet access was essential for their inclusion in the protocol. Patients with severe mental diseases and patients with severe commodities like cancer, or renal failure were excluded from the study. The randomization was based on the order of the appointments, with the odd numbers being assigned to the control group and the evens to the intervention group.The measurements of the individuals who participated in the study included the calculation of BMI, current and mean waist circumference, glycosylated hemoglobin, blood pressure, cholesterol, and questionnaires. All the participants were given questionaries concerning quality of life [For the participants in the control group, a traditional health promotion model was followed. Printed materials were sent every 3 months, and in small groups 4 lectures was given at 6th months of the intervention. The clinic’s medical staff performed the lectures, which were approximately 45 min long and included general epidemiological information about diabetes and obesity and the importance of a healthy lifestyle. So the overall implication of the control group participants was calculated to be approximately 24 h a year for both synchronous (lectures) and asynchronous activities (study material).For participants in the intervention group, a health promotion program was created according to the goals of the research. For the purposes of the study a website was developed called The website included two knowledge games: According to the instructions given by the researcher to the intervention group, each patient was required to dedicate at least 15 min twice a week, with a minimum interval of 2 days, to complete the 2 games that were developed (each consisting of 10 questions) as part of the study, and to read the texts posted on the website. The purpose of the games was to provide personalized information to each patient, as incorrect answers in the games would contribute to the creation of a corresponding part of the “wall”. The texts were written by the research team and clinical staff, and the average reading time for each text did not exceed 3 min. In addition to these texts, general information promoting healthy eating, exercise, etc., as well as epidemiological data, were posted on the “wall”. The total engagement of participants in the intervention was approximately 25 h per year.Compliance of participants was monitored weekly through Google Analytics. In case of non-compliance, the principal investigator would send a reminder email or SMS. Access to the specially designed platform was available exclusively to each participant for 12 months and could be used at any time, according to their own preference.In particular, the home page of each participant included:
General articles that were common to the whole group, about a healthy lifestyle, smoking, the importance of physical activity, etc.Customized health information based on the incorrect answers from the games.Personalized tips that emerged from the attitudes and beliefs section.This research protocol was approved by the ethics committee of the University General Hospital “Attikon”, and written consent of the participants was obtained. | PMC10069363 |
Statistical analysis | obesity, depression, anxiety, diabetes | OBESITY, DIABETES | The analysis of the data was per protocol. Quantitative variables were expressed as mean (Standard Deviation) or as median (interquartile range). Qualitative variables were expressed as absolute and relative frequencies. Independent samples Student’s t-tests were used for the comparison of mean values between the two groups. For the comparison of proportions chi-square and Fisher’s exact tests were used. Repeated measurements analysis of variance (ANOVA) was adopted to evaluate the changes observed in knowledge scores on diabetes and obesity, attitude score towards fighting obesity, quality life scores, anxiety and depression scales among the two groups over the follow up period. Log transformations were made in case of not normal distribution. Power analysis methodology represented a design, with two groups of the between-subject factor of the studied groups and three levels of the within-subjects factor of time. For this design, 72 participants (36 per group) achieved a power of 0.95 for the between-subjects main effect at an effect size of 0.35; a power of 0.95 for the within-subjects main effect at an effect size of 0.20; and a power of 0.95 for the interaction effect at an effect size of 0.20. All reported p values are two-tailed. Statistical significance was set at p < 0.05 and analyses were conducted using SPSS statistical software (version 22.0). | PMC10069363 |
Discussion | obesity, anxiety, chronic disease, depression, hypertension, diabetes | OBESITY, CHRONIC ILLNESSES, POSITIVE, HYPERTENSION, DISEASE, CHRONIC DISEASES, CHRONIC DISEASE, TYPE 2 DIABETES, COMPLICATIONS, DIABETES | Education as a public health service is of vital importance. Specifically when prevention and treatment are part of a strategy to control chronic conditions such as type 2 diabetes or obesity. Therapeutic patient education [WHO] is considered of primary importance so that patients can first understand the nature of their disease and then be empowered with knowledge and skills to manage their symptoms, especially in chronic conditions [Health promotion interventions are considered effective in preventing and improving the overall health of the target population. However, their range could be made more effective through internet-based programs from authorized agencies, taking into account users’ actual needs and expectations [Evidence for the impact of these applications on users’ lifestyles is compelling, according to a 2019 Xinghan et al. meta-analysis of more than 23 studies [These applications could improve health promotion by facilitating interdisciplinary collaboration, better doctor and patient communication, exchange of ideas between patients and targeted information [The aim of this study was to provide targeted and personalized information to participants according to their knowledge deficiencies. Participants answered a series of questions through interactive games and depending on the wrong answers they received the corresponding information. Using this model, a significant improvement in their knowledge scales was recorded compared to the traditional model of health promotion used in the control group. In Garrett et al. published a randomized study in people with diabetes using a The importance of the interactive educational process that takes place through these applications is great and is highlighted by research such as a systematic review of Norris et al., in 2001. This study focused on diabetes self-management education through patients collaborating with others in the same group [The development of various applications that are used daily by millions of users enables direct communication between people of different age groups but who have something in common, such as a health condition. Indeed, a meta-analysis by Laranjo et al. in 2015 showed that, with widespread social media such as Twitter and Facebook, the effect on behavior modification was greater since these networks make it easier to communicate in private groups [Positive attitudes towards chronic illnesses can help to alleviate anxiety and depression, which are major factors influencing quality of life. [Providing information and encouraging optimism in patients with chronic diseases, such as diabetes, also helps to change their attitudes and beliefs and consequently to improve the compliance with the treatment. In giving patients more control over their treatment, we can reduce potential complications from the disease [In 2004, Wassenberg et al. showed that positive attitudes in people with hypertension towards their chronic disease resulted in better regulation of the disease, as participants had more consistent monitoring of blood pressure [Another study by Baranowski et al. in 2016, in collaboration with many organizations, identified the most beneficial features for health education games: that they be interactive, have feedback, allow for agency and control, identity, and immersion [In the present research these elements were included in the development of the two online knowledge games. Greater knowledge resulting in behavior modification was observed since the participants received direct information about their performance (score) as well as customized information depending on their incorrect answers. Indeed, the value of this approach can also be seen in publications such as a study by Espinosa-Curiel et al. in 2020, which developed an online knowledge game aimed at changing children’s eating habits. In just 6 weeks there was an improvement in the level of knowledge as well as a more positive attitude towards a healthier diet. The children’s parents agreed that the game had a positive effect on them as they could now recognize more than 10 unhealthy foods [Like most studies performed with questionaries, the present study has some limitations that should be considered. Apart from the limited sample size, limited depth of information, and possible misunderstandings or interpretation errors from the participants, mainly due to language barriers. The main limitation is the self-report bias. Questionnaires rely on self-reported data, and sometimes the participants may not always be honest or accurate in their responses. In addition, they may be influenced by social desirability bias or other factors affecting their answers, so researchers have limited control over variables that could affect the results. While questionnaires can provide valuable insights into participants’ knowledge, attitudes, and beliefs, they are not without limitations, and their findings should always be considered in the context of their limitations. | PMC10069363 |
Acknowledgements | We thank the medical personnel of the Endocrine Unit, of the 2nd Propaedeutic Department of Internal Medicine, “Attikon” University Hospital,l for their contribution to the study and most specifically Ms Stefania Tsouknida for techical assistance and Ms Georgia Isari, for dietary and further scientific contribution. | PMC10069363 | ||
Author Contribution | MP | MC was the principal investigator and wrote the main manuscript, MP supervised the study design, the data collection and the research process, IM, and GD reviewed the manuscript. | PMC10069363 | |
Funding | This is a self-funded study, and no extra funding was used. | PMC10069363 | ||
Data Availability | The datasets used and analysed during the current study are available from the corresponding author upon reasonable request. | PMC10069363 | ||
Declarations | PMC10069363 | |||
Ethics approval and consent to participate | This study was conducted in full accordance with all applicable research policies, and all methods were carried out in accordance with relevant guidelines and regulations.The study was performed following the protocol that the National and Kapodistrian Athens University Medical School, Ethics and licensing committee approved.All patients sign an informed consent form, before being included in the research groups for participating and publishing the data. The consent form was reviewed and approved from the ethic committee of the hospital. | PMC10069363 | ||
Consent for publication | Not Applicable (NA). | PMC10069363 | ||
Competing Interest | All authors declare that they don’t have any conflict of interest. | PMC10069363 | ||
References | PMC10069363 | |||
Background | eHealth approaches show promising results for smoking cessation (SC). They can improve quit rates, but rigorous research is sparse regarding their effectiveness and the effects of their interactivity, tailoring, and use intensity. | PMC10698653 | ||
Objective | We examined the effectiveness of | PMC10698653 | ||
Methods | REGRESSIONS | Individuals who smoke were randomized into the IG (563/1115, 50.49%) or CG (552/1115, 49.51%), which received a noninteractive, nontailored, and information-only web-based intervention. Data were collected before the intervention, at the postintervention time point (T1), at the 4-month follow-up (T2), and at T3. We tested hypothesis 1 through equivalence tests between the IG’s success rate and success rates of comparable effective interventions reported in 2 current meta-analyses. For hypothesis 2, we conducted binary logistic regressions. For hypothesis 3, we assigned the IG participants to 1 of 4 user types and used binary logistic regressions with user types as the independent variable and smoking abstinence as the dependent variable. | PMC10698653 | |
Results | In the IG, 11.5% (65/563) and 11.9% (67/563) of participants were smoke free at T1 and T3, respectively. These values were statistically equivalent to the effects in the 2 meta-analyses, which reported 9% ( | PMC10698653 | ||
Conclusions | TK-SCC is effective for SC. However, its superiority compared with a minimal SC intervention could not be confirmed in the long term. Insufficient implementation of the techniques used and cotreatment bias could explain this outcome. Higher use intensity of TK-SCC was positively related to abstinence. Therefore, additional efforts to motivate users to adhere to intervention use as intended could improve the intervention’s effectiveness. | PMC10698653 | ||
Trial Registration | German Clinical Trials Register DRKS00020249, Universal Trial Number U1111-1245-0273; https://drks.de/search/de/trial/DRKS00020249 | PMC10698653 | ||
International Registered Report Identifier (IRRID) | RR2-10.1186/s13063-021-05470-8 | PMC10698653 | ||
Introduction | PMC10698653 | |||
Background | RISK OF PREMATURE DEATH | Smoking cessation (SC) reduces the risk of premature death by 40% if individuals who smoke quit before 60 years of age and by 90% if individuals who smoke quit before 40 years of age [eHealth approaches are highly promising because they provide a readily available, low-cost treatment option. eHealth uses electronic communication and information technologies to promote health behaviors, behavior changes, and psychoeducation [Possible ways to increase the effectiveness of eHealth interventions include the incorporation of interactivity and tailoring (personalization of an intervention). In a recent meta-analysis, tailored or interactive internet interventions were not found to be superior compared with other internet interventions [Overall, the demand for rigorous randomized controlled trials (RCTs) of eHealth interventions for SC is high. In particular, evidence regarding the effectiveness of given interventions, evidence of the effects of interactivity and tailoring, and evidence of the effect of use intensity are needed. | PMC10698653 | |
Purpose and Hypotheses | The purpose of the research presented in this paper was to examine the effectiveness of a tailored and interactive internet-based intervention (intervention group [IG]) compared with a noninteractive, nontailored, and information-only internet-based intervention (CG) for SC in a sample of individuals who smoke. In the IG, we expected a clinically relevant number of participants to remain abstinent at the 12-month follow-up (T3; hypothesis 1). Furthermore, we assumed the number of abstinent participants will be significantly greater in the IG than in the CG after 1 year (hypothesis 2). In addition, we predicted that a higher dose (a more intense use of the internet-based health coach) will be positively related to a higher number of successful quitting attempts in the IG (hypothesis 3). | PMC10698653 | ||
Methods | PMC10698653 | |||
Trial Background | Our study was part of a larger project aiming to evaluate an internet-based health coach [ | PMC10698653 | ||
Ethical Considerations | This study followed the principles of the Declaration of Helsinki. It received a favorable opinion from the ethics commission of Albert-Ludwigs University, Medical Center, Freiburg (vote 237/19) on July 25, 2019. Informed consent of all the participants was acquired before their participation. Data protection for the study participants was guaranteed in accordance with the European General Data Protection Regulation. Staff members involved in the project were committed to such data protection through their institutions. All personal data concerning the participants were recorded and stored separately from pseudonymized research data by Vilua Healthcare GmbH. Vilua shared only pseudonymized or aggregated research data with Section of Health Care Research and Rehabilitation Research (SEVERA), Institute of Sport and Sport Science (IfSS), and Techniker Krankenkasse (TK). As compensation, the participants received shopping vouchers worth up to €30 (US $31.7) as well as additional discount vouchers (also refer to | PMC10698653 | ||
Interventions | The 2 interventions used in the IG and CG, respectively, were both developed by a German statutory health insurance and had to be accessed via a web browser on the participants’ personal electronic devices. Participants in the IG had access to a version of TK-SCC [In the CG, participants had access to a nontailored, noninteractive, and internet-based health program. It comprised evidence-based information divided into different lessons and advice on how to obtain smoking abstinence. It did not contain videos; the information provided was less detailed compared with the information provided to the IG, no feedback was given, and no prompts for using the program were issued to the participants in case of inactivity. No customized telephone counseling was offered, but the availability of a public offer for counseling was pointed out. | PMC10698653 | ||
Recruitment of Participants and Data Collection | Universität Düsseldorf | RECRUITMENT | Sample size calculations were performed via G*Power (Universität Düsseldorf) [Between January and September 2020, we conducted recruitment campaigns in Germany using various media (Google marketing campaigns, communication channels of the Techniker health insurance fund and the University of Freiburg, flyers, local newspapers, radio channels, and print magazines). Data collection lasted until January 10, 2022. All adults who smoked, regardless of their health insurance supplier, were eligible for participation. The blinding of the participants could not be ensured, as both interventions were described in the study information. Prior to randomization, all participants were informed that those assigned to the CG would be granted access to TK-SCC upon the completion of the study (after the collection of T3 data). Blinded data analysis could not be achieved because the structure and content of the data indicated group allocation.After completing the baseline questionnaire, participants were assigned to the IG and CG via permuted block randomization with block sizes of 4, 6, and 8 ( | PMC10698653 |
Data Cleansing, Missing Values, and Data Analysis | REGRESSION, SECONDARY | We cleaned the collected data to prevent the biasing of our results. During this process, we excluded cases with implausible values and multivariate outliers (through the comparison of the squared Mahalanobis distance of the items of each case with a chi-square distribution, exclusion of case if All remaining randomized participants were included in the intention-to-treat (ITT) analysis, making the replacement of missing values necessary in case of the main outcome variable. Missing values were primarily generated when the participants either completely dropped out of the study following T0 or when they selectively missed 1 or 2 of the 3 consecutive questionnaires.Missing data points were coded as smoking (Our primary outcome, For the secondary outcomes (To test for a correlation between the use intensity and effect of TK-SCC (hypothesis 3), we assigned the IG participants to certain user types based on the number of their log-ins and the type of their use behavior. First, we made a rough distinction between 2 log-in groups: onetime users with only 1 log-in over the intervention period and multitime users with multiple log-ins over the intervention period. Following this, the multitime users were further subdivided into 3 classes by means of a latent class analysis. For this analysis, the dichotomized use of TK-SCC at the weekly level (0=no use and 1=use) was used to generate categorical indicator variables. The final model was selected based on the best model fit, which was determined using various parameters (eg, group size of the classes found, Bayes information criterion, and entropy). Finally, the following 4 log-in classes emerged: onetime users, rare users (who used the coaching only during the first week), half-time users (who ceased use after half of the intervention period), and constant users (who used TK-SCC throughout the intervention period). The latter case represents the use intended by the developers of the program (use per protocol). To test hypothesis 3, binary logistic regression with user types as the independent variable and smoking abstinence as the dependent variable was used.In all cases of hypothesis testing, we controlled the familywise error rate using the Bonferroni-Holm method [ | PMC10698653 | |
Results | PMC10698653 | |||
Discussion | PMC10698653 | |||
Principal Findings | We compared the effectiveness of a tailored and interactive eHealth intervention for SC with that of a noninteractive, nontailored, and information-only internet-based intervention.In the IG, 11.9% (67/563) of the participants reported 30-day abstinence at 12 months. Although comparisons with different trials have to be made with caution, this abstinence rate was statistically equivalent to the average abstinence rates for similar interventions (9% and 10.9%) reported in 2 recent meta-analyses that we used as reference points [First, TK-SCC is not effective enough to achieve superiority compared with minimal SC support. In particular, the effects of tailoring and interactivity could be less important than previously theorized, or the implementation of these components could have been executed insufficiently. The same argument holds for the implementation of the BCTs used in TK-SCC. In this case, a critical examination and revision of TK-SCC would be required to improve its effectiveness. As a first step in this direction, we conducted a detailed qualitative analysis of the accounts of TK-SCC users [Second, the intervention used in the CG was more effective than minimal SC support. Indeed, 8.2% (45/552) of the participants in our CG were smoke free at T3. This abstinence rate is in line with the average effects of “other SC support alone,” “lower intensity SC support,” and “smartphone app” (with each having an 8% abstinence rate 6 to 12 months after the intervention), whereas “minimal SC support” lead to a 6% abstinence rate, on average, in the aforementioned meta-analysis [In addition to an intervention effect and a difference between the groups, we predicted that a more intense use of TK-SCC would be positively related to a higher proportion of participants who successfully quit smoking. After identifying 4 user groups based on their use behavior, at the end of treatment, half-time users were more successful than participants who used TK-SCC less frequently; however, this effect did not last over time. Nevertheless, constant use (the use intended by the program developers) was strongly associated with smoking abstinence even 1 year after the end of treatment. | PMC10698653 | ||
Strengths and Limitations | RECRUITMENT | We conducted a study with a thorough methodology (with implementation as an RCT, the coding of missing participants as participants who still smoke, a long period between the end of program and the last measurement point, the incorporation of various confounders, and α error correction for multiple testing). Furthermore, we were able to recruit a large sample of participants via various recruitment channels.The limitations of our work are the lack of blinding of the participants; the considerable dropout rate (albeit this is common for internet-based interventions [When planning a comparable study, future experimental designs should acknowledge the possibility that a relevant proportion of the participants will use additional eHealth interventions, which could complicate proving an existing intervention effect. We have 2 suggestions regarding this concern. First, the use of additional SC interventions should be systematically assessed and considered as a covariate. Second, the handling of participants with missing data as participants who still smoke ( | PMC10698653 | |
Comparison With Prior Work | The effects in our IG were statistically equivalent to the average effects of SMS text messaging interventions and internet-based programs that were either tailored, interactive, or both, as reported in 2 meta-analyses (9% and 10.9% smoke free after 6 to 12 months, respectively [ | PMC10698653 | ||
Conclusions | Robin Anger | Our study adds to the existing evidence that eHealth SC interventions using BCTs, interactivity, and tailoring can be effective tools for increasing the quit rates of individuals who smoke, but superiority compared with a less intensive intervention could not be proven in the long term. Insufficient implementation of the techniques used in TK-SCC (interactivity, tailoring, or the used BCTs) as well as cotreatment bias could explain this outcome. Further analysis of the implementation of the techniques used in TK-SCC, possibly succeeded by program revision, seems promising. However, a more intense use of TK-SCC was positively related to a higher number of successful quitting attempts. Therefore, our results support the theory that additional efforts to keep the users of eHealth SC programs engaged might contribute to improving the effectiveness of these interventions. A suggested strategy could be the implementation of gamification [The authors would like to acknowledge Robin Anger for his support at the beginning of this project, as well as Clara Franck for her support in the literature research for this paper. Furthermore, the authors would like to thank Rainer Bredenkamp, Irina Kopman, Kerstin Hofreuter-Gätgens, Nicole Knaack, and Dagmar Köppel for their engagement during the entire study.The trial presented in this paper was independently conceptualized and conducted by the Section of Health Care Research and Rehabilitation Research (SEVERA), Medical Center Freiburg, and the Institute of Sport and Sport Science (IfSS), both affiliated to the University of Freiburg. The Techniker Krankenkasse Smoking Cessation Coaching [Authors' Contributions: EF-G, IT, MB, PL, GM, and CS contributed to the conceptualization and design of the analysis. EF-G contributed to supervision. IT and PM contributed to project administration. MS, IT, PM, and CS contributed to data collection. MS contributed data or analysis tools. PM performed the analysis. PM wrote the original draft. All the authors reviewed and edited the manuscript.Conflicts of Interest: PL received funding for content production and further development of Techniker Krankenkasse Smoking Cessation Coaching from Techniker Krankenkasse. All other authors declare no other conflicts of interest.CONSORT EHEALTH checklist (V 1.6.1). | PMC10698653 | |
Abbreviations | behavior change techniquecomplete casecontrol groupConsolidated Standards of Reporting Trials of Electronic and Mobile Health Applications and Online TelehealthFagerström Test for Cigarette DependenceInstitute of Sport and Sport Scienceintervention groupintention-to-treatodds ratiorandomized controlled trialsmoking cessationSection of Health Care Research and Rehabilitation ResearchTechniker KrankenkasseTechniker Krankenkasse Smoking Cessation Coaching | PMC10698653 | ||
Data Availability | The data sets generated or analyzed during this study are available from the corresponding author upon reasonable request. The prerequisite for this is that the anonymity rules promised to the participants are adhered to (eg, no publication of data that allows drawing conclusions about the identity of a person). | PMC10698653 | ||
Key Points | PMC10134010 | |||
Question | myopia | MYOPIA | Does a repeated low-level red-light (RLRL) intervention prevent incident myopia among children with premyopia? | PMC10134010 |
Findings | myopia | MYOPIA | In this randomized clinical trial including 278 school-aged children with premyopia, the incidence of myopia was lower among children receiving RLRL therapy than among controls. | PMC10134010 |
Meaning | myopia | MYOPIA | Exposure to RLRL is a novel and effective intervention for myopia prevention among children with premyopia, with good user acceptability and safety. | PMC10134010 |
Importance | myopia, Myopia | MYOPIA, MYOPIA | Myopia is a global concern, but effective prevention measures remain limited. Premyopia is a refractive state in which children are at higher risk of myopia, meriting preventive interventions. | PMC10134010 |
Objective | myopia | MYOPIA | To assess the efficacy and safety of a repeated low-level red-light (RLRL) intervention in preventing incident myopia among children with premyopia. | PMC10134010 |
Design, Setting, and Participants | This was a 12-month, parallel-group, school-based randomized clinical trial conducted in 10 primary schools in Shanghai, China. A total of 139 children with premyopia (defined as cycloplegic spherical equivalence refraction [SER] of −0.50 to 0.50 diopter [D] in the more myopic eye and having at least 1 parent with SER ≤−3.00 D) in grades 1 to 4 were enrolled between April 1, 2021, and June 30, 2021; the trial was completed August 31, 2022. | PMC10134010 | ||
Interventions | Children were randomly assigned to 2 groups after grade stratification. Children in the intervention group received RLRL therapy twice per day, 5 days per week, with each session lasting 3 minutes. The intervention was conducted at school during semesters and at home during winter and summer vacations. Children in the control group continued usual activities. | PMC10134010 | ||
Main Outcomes and Measures | myopia | MYOPIA | The primary outcome was the 12-month incidence rate of myopia (defined as SER ≤−0.50 D). Secondary outcomes included the changes in SER, axial length, vision function, and optical coherence tomography scan results over 12 months. Data from the more myopic eyes were analyzed. Outcomes were analyzed by means of an intention-to-treat method and per-protocol method. The intention-to-treat analysis included participants in both groups at baseline, while the per-protocol analysis included participants in the control group and those in the intervention group who were able to continue the intervention without interruption by the COVID-19 pandemic. | PMC10134010 |
Results | myopic, myopia | MYOPIA, SECONDARY | There were 139 children (mean [SD] age, 8.3 [1.1] years; 71 boys [51.1%]) in the intervention group and 139 children (mean [SD] age, 8.3 [1.1] years; 68 boys [48.9%]) in the control group. The 12-month incidence of myopia was 40.8% (49 of 120) in the intervention group and 61.3% (68 of 111) in the control group, a relative 33.4% reduction in incidence. For children in the intervention group who did not have treatment interruption secondary to the COVID-19 pandemic, the incidence was 28.1% (9 of 32), a relative 54.1% reduction in incidence. The RLRL intervention significantly reduced the myopic shifts in terms of axial length and SER compared with the control group (mean [SD] axial length, 0.30 [0.27] mm vs 0.47 [0.25] mm; difference, 0.17 mm [95% CI, 0.11-0.23 mm]; mean [SD] SER, –0.35 [0.54] D vs –0.76 [0.60] D; difference, –0.41 D [95% CI, –0.56 to –0.26 D]). No visual acuity or structural damage was noted on optical coherence tomography scans in the intervention group. | PMC10134010 |
Conclusions and Relevance | myopia | MYOPIA | In this randomized clinical trial, RLRL therapy was a novel and effective intervention for myopia prevention, with good user acceptability and up to 54.1% reduction in incident myopia within 12 months among children with premyopia. | PMC10134010 |
Trial Registration | myopia | MYOPIA | ClinicalTrials.gov Identifier: This randomized clinical trial assesses the efficacy and safety of a repeated low-level red-light intervention in preventing incident myopia among children in China with premyopia. | PMC10134010 |
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