Noncompliance with self-care behavior and associated factors among adult patients with heart failure in Ethiopia: A systematic review and meta-analysis

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In Ethiopia, more than one-third of heart failure patients die annually. Although several primary studies have been conducted to determine the prevalence of noncompliance with self-care behavior among adult patients with heart failure in Ethiopia, these studies have reported different findings. Consequently, this study aimed to determine the pooled prevalence of noncompliance with self-care behavior among patients with heart failure in Ethiopia. Methods : Searches were performed on the Google Scholar search engine, Scopus, PubMed, and Web of Science databases for relevant studies. A random-effects DerSimonian-Laird model was used to calculate the pooled prevalence of noncompliance with self-care behavior. Sensitivity and subgroup analyses were performed to control marked heterogeneity, and a funnel plot was used to assess publication bias. Results : A total of 17 primary studies with a sample size of 5,528 participants were included in the final meta-analysis. The pooled prevalence of noncompliance with self-care behavior among adult patients with heart failure was 59.70% (95% CI: 54.08, 65.32); I 2 = 95.00%; P < 0.001). Comorbidities [AOR = 2.40, 95% CI: 1.88, 3.07]; depression [AOR = 2.70, 95% CI: 1.76, 4.14]; educational level (primary school or lower) [AOR = 1.72, 95% CI: 1.28, 2.30]; and poor social support [AOR = 2.77, 95% CI: 1.53, 5.03] were significantly associated with noncompliance with self-care behavior. Conclusions : The pooled prevalence of noncompliance with self-care behavior among heart failure patients was considerably high in Ethiopia. Comorbidities, depression, educational level (primary school or lower), and poor social support were the pooled independent predictors of noncompliance with self-care behavior in Ethiopia. Therefore, special attention should be given to patients with these identified risk factors. Moreover, healthcare providers should encourage heart failure patients to comply with all the recommended components of self-care behavior. Cardiac & Cardiovascular Systems Noncompliance Self-care Heart failure Ethiopia Meta-analysis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Introduction Heart failure (HF) is a major public health concern and a leading cause of death among adults and elderly individuals [ 1 , 2 ], affecting more than 37.7 million individuals worldwide [ 2 , 3 ]. Nearly 1 million new cases are diagnosed annually, which is expected to increase by 46% by 2030 [ 4 – 6 ]. The majority of patients with HF are three to four times more common in Africa than in Western countries [ 7 ]. In particular, in sub-Saharan Africa (SSA), HF has significant public health importance due to its greater impact on young economically active individuals, resulting in substantial disability, premature death, and loss of economic productivity [ 8 ]. The rates of HF-associated deaths were greater in low- and middle-income countries (LMICs) than in high-income countries (HICs) [ 9 ]. In Ethiopia, 30–40% of heart failure patients die annually [ 10 ]. HF costs approximately 108 billion dollars of global health-related expenditures annually, and the majority of costs are related to hospital care [ 4 ]. Self-care behavior (SCB) is a "naturalistic decision-making process" in which individuals maintain health through health-promoting practices and managing illness [ 11 – 13 ]. HF patients who are compliant with SCB require self-medication, weight monitoring, salt restriction, fluid limitations, regular exercise, hygiene, early treatment after symptoms occur, and keeping appointments [ 14 – 16 ]. Compliance with SCB has a key role in improving patients’ quality of life, reducing both economic and personal burdens, facilitating early detection of clinical problems, lowering mortality, and reducing the risk of rehospitalization for HF [ 17 , 18 ]. The occurrence of noncompliance with SCB is becoming a major problem in developed and developing countries [ 19 – 23 ]. A wide range of risk factors affect the compliance of HF patients with the recommended components of SCB. Patients’ characteristics such as age, sex, marital status, religion, place of residence, educational levels, occupation, and family income, and clinical characteristics like duration of diagnosis, stage of HF, comorbidity, prior hospitalization, awareness of heart failure, presence of depressive symptoms, and social support [ 10 , 24 – 28 ]. Although several primary studies have been conducted to determine the prevalence of noncompliance with SCB among adult patients with HF in Ethiopia, these studies have reported different findings and have shown epidemiological variations ranging from 37.4% [ 29 ] to 77.70% [ 27 , 30 ]. Consequently, this study aimed to determine the pooled prevalence of noncompliance with SCB and identify the associated factors among adult patients with HF in Ethiopia. The findings of this study play a key role for stakeholders and policymakers, giving strong evidence of noncompliance with SCB among HF patients to plan intervention programs. Methods Reporting and registration protocol The Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) framework [31] was used to conduct this study (Supplemental Table 1). The review protocol was registered with the Prospero database: www.crd.york.ac.uk/PROSPERO/display_record.asp?ID=CRD42023450821. Search strategy The adapted PICO format was used to retrieve the relevant studies. The PICO consists of population (P), exposure (E), context (C), and outcome (O) as detailed below. Population : Adult patients with HF. Exposure : Associated factors, risk factors, determinants, and predictors i.e. age, sex, marital status, religion, place of residence, educational levels, occupation, and family income of the patients, duration of diagnosis, stage of HF, comorbidities, prior hospitalization, awareness of heart failure, presence of depressive symptoms, and social support. Context : Ethiopia; Addis Ababa, Amhara, Oromia, southern nations, nationalities and peoples, Sidama, and Tigray regions. Outcome : Noncompliance with SCB. Using the above PICO, we developed the following review questions which focused on identifying all the relevant studies. What is the prevalence of noncompliance with SCB among HF patients in Ethiopia? What are the factors associated with noncompliance with SCB among HF patients in Ethiopia? We searched the Google Scholar search engine, Scopus, PubMed, and Web of Science databases, the University’s Research Repository Online Library, citation tracking, and manual search of reference lists for relevant studies that reported the prevalence of noncompliance with SCB and associated factors among adult patients with HF using the following MeSH terms: (ʺNoncomplianceʺ [MeSH term] OR ʺNonadherenceʺ [MeSH term] OR ʺComplianceʺ [MeSH term] OR ʺAdherenceʺ [MeSH term]) AND (ʺSelf-care behaviorʺ [MeSH term] OR ʺSelf-care practiceʺ [MeSH term]) AND (ʺPredictorsʺ [MeSH term] OR ʺAssociated factorsʺ [MeSH term] OR ʺRisk factorsʺ [MeSH term] OR ʺDeterminantsʺ [MeSH term]) AND ʺEthiopiaʺ (Supplemental file 1). Two independent authors (TMA and YMT) participated in searching for relevant studies from December 12-18/2023. The search string was developed using ʺANDʺ and ʺORʺ Boolean operators. The included studies were published in English between 2014 and 2022 in Ethiopia. Eligibility criteria All observational studies that were conducted among adult (aged ≥18 years ) patients with heart failure in Ethiopia and reported the prevalence of noncompliance to SCB and/or at least one factor associated with noncompliance with SCB among adult patients with HF and published in English were included in the study. However, articles without abstracts and/or full texts, systematic reviews, meta-analyses, or qualitative studies were excluded from the study. Study selection All the accessed studies were exported to the EndNote version 7 reference manager to remove duplicate studies. First, two independent investigators (TMA and SDK) screened the titles and abstracts, followed by the full-text reviews to determine the eligibility of each study. Discrepancies between the investigators were solved through dialog. Data extraction The two independent reviewers (TMA and WNA) extracted the data using structured Microsoft Excel. When discrepancies were observed in the extracted data, the phase was repeated. When discrepancies between the extracted data continued, the third reviewer (SA) participated. The name of the first author, year of publication, study area, study design, sample size, response rate, and effect size of the included primary studies were extracted. Since all the included studies were cross-sectional, the study could not perform a subgroup analysis according to the study design. Moreover, there were inconsistencies in the outcome measurement across the included studies. Outcome measures This study focused on two main outcome measures. The primary outcome of interest was the prevalence of noncompliance with SCB among adult patients with HF. The second outcome was identifying factors associated with noncompliance with SCB among adult patients with HF. Operational definition Noncompliance with SCB is "an inability to promote health, prevent disease, maintain health, and cope or deal with illness and disability with or without the support of a healthcare provider" [15]. Noncompliance with SCB is appreciated if less than the midpoint (50%) of the revised nine-item European Heart Failure Self-care Behavior Scale (EHFScBS-9) [21, 32]. Data analysis The extracted data were exported to STATA version 17 for analysis. The pooled prevalence of noncompliance with SCB was estimated using a random-effects DerSimonian-Laird model [33]. A funnel plot was used to assess publication bias. Asymmetry of the funnel plot is an indicator of publication bias, and Egger’s test with a p-value of <0.05 was used to determine a significant publication bias [34]. The heterogeneity across the included primary studies was assessed using I 2 statistics [35]. The I 2 statistics ranged from 0 to 100%, and I 2 values of 0, 25, 50, and 75% were considered to indicate no, low, moderate, and high degrees of heterogeneity respectively [35]. A p-value of the I 2 statistic <0.05 was used to confirm substantial heterogeneity [36, 37]. Sensitivity analysis was employed to determine the effect of a single study on the overall estimate. A forest plot was generated to estimate the influence of associated factors on the outcome variable, and the 95% CI was calculated. The adjusted odds ratio (AOR) was the measure of association in the included studies. Results Search results The search strategy identified 1,296 studies from PubMed (669), Google Scholar (578), Scopus (33), Web of Science (10), manual search (1), and the University’s Research Repository Online Library (5) studies. After removing the irrelevant studies based on their titles and abstracts (n=968) and duplicated studies (n=74), a total of 254 studies were included in the full-text review. Subsequently, full-text reviews were conducted, resulting in the removal of 203 studies because of a lack of complete texts. Then, 51 studies were assessed for full-text reviews, and 34 studies were excluded. Finally, 17 studies were found to be applicable for determining the pooled prevalence of noncompliance with SCB and identifying its associated factors. The PRISMA flow chart [38] was constructed to show the selection process (Figure 1). Characteristics of the included primary studies All 17 [10, 24-30, 39-47] studies were cross-sectional. Concerning geographical region, five studies [24, 26, 29, 40, 41] were conducted in Oromia; four [27, 30, 42, 46] in Amhara, three [10, 39, 45] in Addis Ababa, two [43, 44] in south nations, nationalities and people, two [28, 47] in Sidama and one [25] in Tigray. The total sample size of the included primary studies was 5,528, where the smallest (229) and the largest (424) sample sizes were obtained in the Sidama and Oromia regions respectively. The prevalence of noncompliance with SCB was obtained from all seventeen included primary studies [10, 24-30, 39-47], and the data regarding associated factors were obtained from the fifteen primary studies [10, 24-30, 39, 41-44, 46, 47], with a response rate ranging from 95.3 to 100% (Table 1). Table 1 : Summary of the included primary studies, 2023 ID Author [Year] Study area Study design Measuring tool Mean age Sample size Prevalence (95% CI) Quality Assen M 2017 [30] Amhara CS EHFScBS-6 49.0 310 77.70(73.07,82.33) Low risk Baymot A 2022 [39] A.A CS EHFScBS-6 45.03 294 67.30(61.94, 72.66) Low risk Beker J 2014 [24] Oromia CS EHFScBS-11 48.76 255 59.20(53.17, 65.23) Low risk Fetensa G 2017 [40] Oromia CS EHFScBS-6 48.08 424 48.80(44.04, 53.56) Low risk Getachew A 2022 [41] Oromia CS EHFScBS-11 47.42 420 46.40(41.63, 51.17) Low risk Hailu Gebru T 2021 [25] Tigray CS EHFScBS-11 45.4 408 54.20(49.37, 59.03) Low risk Molla B 2022 [42] Amhara CS EHFScBS-6 55.7 304 67.10(61.82, 72.38) Low risk Mulugeta T 2022 [26] Oromia CS EHFScBS-6 45.0 266 50.0(43.99, 56.01) Low risk Seid MA 2019 [27] Amhara CS EHFScBS-6 49 310 77.70(73.07, 82.33) Low risk Sewagegn N 2015 [29] Oromia CS EHFScBS-6 52.02 328 37.40(32.16, 42.64) Low risk Shafi Surur H 2022 [43] SNNP CS EHFScBS-11 45.0 292 64.70(59.22, 70.18) Low risk Sigebo E 2022 [44] SNNP CS EHFScBS-11 N.A 240 58.30(52.06, 64.54) Low risk Sitotaw E 2022 [28] Sidama CS EHFScBS-9 49 229 65.90(59.76, 72.04) Low risk Tegegn BW 2021 [10] A.A CS EHFScBS-6 N.A 396 72.0(67.58, 76.42) Low risk Tole S 2015 [45] A.A CS EHFScBS-11 N.A 384 58.0(53.06, 62.94) Low risk Yazew KG 2017 [46] Amhara CS EHFScBS-11 52.3 403 62.30(57.57, 67.03) Low risk Zewdu Agegnehu 2022 [47] Sidama CS EHFScBS-11 N.A 265 47.30(41.29, 53.31) Low risk Abbreviations: AA, Addis Ababa; CS, cross-sectional; EHFScBS, European Heart Failure Self-care Behavior Scale ; N.A , not available ; SNNP , southern nations, nationalities and peoples. Quality assessment of the included primary studies Two independent authors (TMA and SDK) assessed the quality of the included primary studies. The quality of each study was appraised using the Joanna Briggs Institute (JBI) quality assessment criteria [48]. All the included 17 primary studies [10, 24-30, 39-47] were assessed using the JBI checklist for a cross-sectional study. Of the 17 studies, twelve scored seven of the eight questions, 87.5% (low risk), three scored six of the eight questions, 75% (low risk), and the remaining two scored five of the eight questions, 62.5% (low risk) (Supplemental Table 2). Studies were considered to be of low risk when they scored 50% or higher on the quality assessment indicators. The studies scored between 5 and 7 out of a total of 8 points. Thus, all the included primary studies [10, 24-30, 39-47] were of good quality. Risk of bias assessment of the included primary studies The standardized assessment tool [49] was used to evaluate the risk of bias. Accordingly, of the total of the seventeen included studies, fourteen scored eight of the ten questions and three studies scored seven of the ten questions. Studies were classified as ʺlow riskʺ if eight and above of the ten questions received ʺYesʺ, as ʺmoderate riskʺ if six to seven of the ten questions received ʺYesʺ and as ʺhigh riskʺ if five or lower of the ten questions received ʺYesʺ. As a result, all the included primary studies [10, 24-30, 39-47] had a low risk of bias (good quality) (Supplemental Table 3). Pooled prevalence of noncompliance with self-care behavior A total of 17 eligible primary studies [10, 24-30, 39-47] with 5,528 participants were included in the study. In this meta-analysis, the pooled prevalence of noncompliance with SCB among adult patients with HF was 59.70% (95% CI: 54.08, 65.32); I 2 =95.00%; P<0.001) (Figure 2). Publication bias The funnel plot showed a symmetrical distribution (Figure 3), and the p-value of Egger's test was 0.2976, which indicated the absence of publication bias in the study. Investigation of heterogeneity The percentage of I 2 statistics in the forest plot showed significant heterogeneity among the included primary studies (I 2 =95.00%; P<0.001) (Figure 2). Therefore, sensitivity and subgroup analyses were performed to manage the heterogeneity. Sensitivity analysis A sensitivity analysis was also conducted to check for the presence of outliers in the included primary studies. The forest plot revealed that the estimate of a single study was closer to the pooled estimate, indicating the absence of a particular study effect on the overall pooled estimate (Figure 4). Subgroup analysis The subgroup analysis was performed based on the study area and measuring tools used. Hence, the highest pooled prevalence of noncompliance with SCB was among those studies conducted in Amhara [71.24, 95% CI: 63.51, 78.96; I 2 =90.30%, P<0.001], and the lowest pooled prevalence was among those studies conducted in Oromia region [48.25, 95% CI: 41.76, 54.74; I 2 =86.61%; P<0.001]. Similarly, the higher pooled prevalence of noncompliance with SCB was among those studies that used a measuring tool of 6-items of European Heart Failure Self-care Behavior Scale (EHFScBS-6) [62.29, 95%CI: 51.93, 72.65; I 2 =97.10%; P<0.001] followed by those studies that used a measuring tool of 11-items of EHFScBS (EHFScBS-11) [56.29, 95% CI: 51.64, 60.94; I 2 =83.65%; P<0.001] (Table 2). Therefore, the heterogeneity of the study could be because of differences in the study area and measuring tools used. Table 2 : Subgroup analyses of noncompliance with self-care behavior among adult patients with heart failure in Ethiopia, 2023 Variables Outcome Subgroup No. of studies Model Prevalence (95% CI) I 2 P-value Study area Noncompliance with SCB A.A 3 Random 65.81(57.48, 74.13) 88.49% <0.001 Amhara 4 Random 71.24 (63.51, 78.96) 90.30% <0.001 Oromia 5 Random 48.25 (41.76, 54.74) 86.61% <0.001 SNNP 2 Random 61.68 (55.42, 67.94) 56.17% <0.13 Sidama 2 Random 56.59 (38.36, 74.82) 94.44% <0.001 Tigray 1 Random 54.20 (49.37, 59.03) 0.00% <1.00 Measuring tool Noncompliance with SCB EHFScBS-6 8 Random 62.29 (51.93, 72.75) 97.10% <0.001 EHFScBS-11 8 Random 56.29 (51.64, 60.94) 83.65% <0.001 EHFScBS-9 1 Random 65.90 (59.76, 72.04) 0% <1.0 Note: A.A, Addis Ababa; CI, confidence interval; EHFScBS, European Heart Failure Self-care Behavior Scale; SNNP, southern nations, nationalities and peoples Factors associated with noncompliance with self-care behavior In this study, eleven studies [10, 24, 25, 27-30, 39, 43, 44, 47] showed a significant association between comorbidities and noncompliance with SCB. The pooled AOR of noncompliance with SCB for HF patients with comorbidities was 2.40 (95% CI: 1.88, 3.07; I 2 =25.89%; P< 0.20) (Figure 5). Six studies [10, 24, 41-43, 46] reported that depression was significantly associated with noncompliance with SCB. The pooled AOR of noncompliance with SCB for patients with depression was 2.70 (95% CI: 1.76, 4.14; I 2 =60.89%; P<0.03) (Figure 6). Ten studies [10, 25-28, 39, 41, 42, 44, 46] showed a significant association between the educational level (primary school or lower) and noncompliance with SCB. The pooled AOR of noncompliance with SCB for patients with educational levels (primary school or lower) was 1.72 (95% CI: 1.28, 2.30; I 2 =24.73%; P<0.22) (Figure 7). Seven studies [25, 39, 41, 42, 44, 46, 47] also reported that poor social support was significantly associated with noncompliance with SCB. The pooled AOR of noncompliance with SCB for patients who had poor social support was 2.77 (95% CI: 1.53, 5.03; I 2 =77.08%; P<0.001) (Figure 8). Discussion The findings of this study indicated that the pooled prevalence of noncompliance with SCB among patients with HF was 59.70% (95% CI: 54.08, 65.32); I 2 =95.00%; P<0.001). The findings of this study also reported that comorbidities, depression, educational level (primary school or lower), and poor social support were significantly associated with noncompliance with SCB among patients with HF. In this study, the pooled prevalence of noncompliance with SCB was 59.70%. This study finding was congruent with those of studies conducted in Brazil (63.5%) [50] and the United States of America (USA) (64.4%) [51]. However, the finding of this study was higher than those studies conducted in Pakistan (43.5%) [52], Vietnam (45.5%) [53], Kenya (50.8%) [20], the Netherlands (52%) [54] and Zimbabwe (53.8%) [55]. Conversely, this study finding was lower than those of studies conducted in Sudan (72%) [56], Iran (74%) [57], Tanzania (74.7%) [58] and India (77%) [59]. These discrepancies might be due to differences in socioeconomic factors, healthcare characteristics, and knowledge levels across the study populations [39, 41, 59]. Furthermore, the findings of the study showed that adult HF patients with comorbidities were 2.40 times more likely to develop noncompliance with SCB than patients without comorbidities. The finding of this study was consistent with those of studies conducted in the USA [60] and the Netherlands [53]. This might be because patients who have comorbid illnesses may take various medications, and physical and disease severity may affect their ability to perform the recommended self-care behaviors [30]. The findings of this study also revealed that HF patients with depression were 2.70 fold more likely to encounter noncompliance with SCB than were their counterparts. The finding of this study was congruent with those of studies conducted in the Netherlands [61] and the USA [62]. The possible reason could be patients with depressive symptoms usually develop altered thinking abilities and unfavorable attitude toward health maintenance and become more noncompliant with SCB recommendations [63]. Similarly, the findings of this study showed that HF patients with educational levels of primary school or lower were 1.72 times more likely to experience noncompliance with SCB than were those with an educational level of secondary school or above. The finding of this study was similar with those of studies conducted in Atlanta, USA [64] and Nepal [65]. This can be explained by individuals with lower educational levels possibly having a lower level of reasoning and decision-making to perform self-care behaviors, leading to noncompliance with SCBs [42, 66]. Similarly, the findings of this study indicated that HF patients with poor social support were 2.77 times more likely to face noncompliance with SCB than patients with good social support. The finding of this study was in line with those of studies conducted in Iran [67], the Netherlands [68], and Korea [69]. Good social support may act as a gentle guiding force that encourages behavioral change for better self-care behavior. Therefore, patients with poor social support cannot be encouraged to engage in better SCB [41, 42]. Strengths and limitations of the study To the best of our knowledge, this systematic review and meta-analysis was the first study to pool the results of several primary studies conducted in Ethiopia, providing stronger evidence of noncompliance with SCB among HF patients. Although all the included studies were of good quality, all the included studies were cross-sectional. Moreover, there were inconsistencies in the outcome measurement across the included studies. Additionally, the study could not perform a subgroup analysis according to the study design. Conclusion The pooled prevalence of noncompliance with SCB among adult patients with HF was substantially high in Ethiopia. The review revealed that comorbidities, depression, educational level of primary school or lower, and poor social support were the pooled independent predictors of noncompliance with SCB among adult patients with HF in Ethiopia. Therefore, special attention should be given to patients with these identified risk factors. Moreover, healthcare providers should encourage HF patients to comply with all the recommended components of SCB. Abbreviations AOR Adjusted odds ratio CI Confidence interval EHFScBS European Heart Failure Self-care Behavior Scale HF Heart failure HICs High-income countries JBI Joanna Briggs Institute LMICs Low- and middle-income countries PRISMA Preferred Reporting Items for Systematic Reviews and Meta-Analyses SCB Self-care behavior SSA Sub-Saharan Africa USA United States of America Declarations Authors’ contributions TMA has generated the idea for the review. BMB, SDK, WNA, SA, SZ, and AK were involved in data collection and statistical analysis. TMA wrote the first draft of the manuscript. GL, BMM, NE, and YMT revised the manuscript. All the authors were responsible for the accuracy of the analysis and the contents of the study. Finally, the authors read and approved the final version of the manuscript for publication. Acknowledgment We would like to extend our gratitude to Mr. Henok Andualem for his unreserved support throughout the study. Funding Not applicable. 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Eur J Cardiovasc Nurs 16(6):530–538 Beker J, Belachew T, Mekonin A, Hailu E (2014) Predictors of adherence to self-care behaviour among patients with chronic heart failure attending Jimma University Specialized Hospital Chronic Follow up Clinic, South West Ethiopia. Cardiovasc Dis Diagnosis 2(6):1–8 Hailu Gebru T, Kidanu Berhe K, Tilahun Tsehaye W, Hagos Mekonen H, Gemechu Kiros K, Gebreslassie Gebrehiwot T, Teklebrhan Gebrehiwot G, Hailesilassie Teka Y, Maeruf Mohammed H (2021) Self-care behavior and associated factors among heart failure patients in Tigray, Ethiopia: a cross-sectional study. Clin Nurs Res 30(5):636–643 Mulugeta T, Duguna D, Bekele A, Umeta B (2022) Adherence to self-care behaviors and associated factors among adult heart failure patients attending chronic follow-up care at Jimma University Medical Center, Southwest Ethiopia. Open Nurs J. ;16(1) Seid MA, Abdela OA, Zeleke EG (2019) Adherence to self-care recommendations and associated factors among adult heart failure patients. From the patients’ point of view. PLoS ONE 14(2):e0211768 Sitotaw E, Tsige Y, Boka A (2022) Practice of self-care behaviours and associated factors among patients with heart failure. Br J Cardiac Nurs 17(1):1–0 Sewagegn N, Fekadu S, Chanie T (2015) Adherence to self-care behaviours and knowledge on treatment among heart failure patients in Ethiopia: the case of a tertiary teaching hospital. J Pharma Care Health Sys 10:2376 Assen M Heart failure patients self-care treatment adherence and associated factors at University of Gondar referal hospital, Northwest Ethiopia (Doctoral dissertation, UOG) Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, Shamseer L, Tetzlaff JM, Akl EA, Brennan SE, Chou R (2021) The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. Int J Surg 88:105906 Jaarsma T, Årestedt KF, Mårtensson J, Dracup K, Strömberg A (2009) The European Heart Failure Self-care Behaviour scale revised into a nine‐item scale (EHFScB‐9): a reliable and valid international instrument. Eur J Heart Fail 11(1):99–105 DerSimonian R, Kacker R (2007) Random-effects model for meta-analysis of clinical trials: an update. Contemp Clin Trials 28(2):105–114 Peters JL, Sutton AJ, Jones DR, Abrams KR, Rushton L (2006) Comparison of two methods to detect publication bias in meta-analysis. JAMA 295(6):676–680 Higgins JP, Thompson SG, Deeks JJ, Altman DG (2003) Measuring inconsistency in meta-analyses. BMJ 327(7414):557–560 Borenstein M, Hedges LV, Higgins JP, Rothstein HR (2010) A basic introduction to fixed-effect and random‐effects models for meta‐analysis. Res synthesis methods 1(2):97–111 Higgins JP, Thompson SG (2002) Quantifying heterogeneity in a meta-analysis. Stat Med 21(11):1539–1558 Stovold E, Beecher D, Foxlee R, Noel-Storr A (2014) Study flow diagrams in Cochrane systematic review updates: an adapted PRISMA flow diagram. Syst reviews 3:1–5 Baymot A, Gela D, Bedada T (2022) Adherence to self-care recommendations and associated factors among adult heart failure patients in public hospitals, Addis Ababa, Ethiopia, 2021: cross-sectional study. BMC Cardiovasc Disord 22(1):1–1 Fetensa G, Waldamichael K, Abera A (2017) Health Seeking Behavior and Associated Factors among Chronic Heart Failure Adult Clients, Jimma University Specialized Hospital, South West Ethiopia. Med Health Sci Res J 1(1):12–20 Getachew A, Assefa T, Negash W (2022) Self-care behavior and associated factors among patients with heart failure in public hospitals of Southeast Ethiopia. J Int Med Res 50(8):03000605221119367 Molla B, Geletie HA, Alem G, Gualu T, Zewudie BT, Tesfa S, Tsehay T, Amlak BT (2022) Adherence to Self-Care Recommendations and Associated Factors among Adult Heart Failure Patients in West Gojjam Zone Public Hospitals, Northwest Ethiopia. International Journal of Chronic Diseases. ; 2022 Shafi Surur H (2022) Adherence to Self-Care Behavior and Its Associated Factors Among Adults with Heart Failure in Gurage Zone Hospitals, Southern Ethiopia, (Doctoral dissertation, HU) Sigebo E, Worku MT, Gobena T (2022) Self-care behavior and associated factors among adult heart failure patients in outpatient cardiac follow-up unit at Wachemo University Nigist Eleni comprehensive specialized hospital, Southern Ethiopia (Doctoral dissertation, Haramaya University) Tole S Assessment of Overall Level of Compliance Self Care and Its Associate Factors to Treatment among Heart Failure Patients in Adult Emrgency Departement and Cardiac Referal Clinic at Tikur Anbesa Specialized Hospital Addis Ababa, Ethiopia (Doctoral dissertation, Addis Ababa University) Yazew KG, Salih MH, Beshah DT (2019) Self-care behavior and associated factors among adults with heart failure at cardiac follow-up clinics in West Amhara Region Referral Hospitals, Northwest Ethiopia, 2017. Int J Afr Nurs Sci 11:100148 Zewdu Agegnehu W (2022) Self-Care Practice and Associated Factors Among Ambulatory Heart Failure Patients in Public Hospitals of Sidama Regional State Ethiopia, 2022, HU Peters MD, Godfrey CM, Khalil H, McInerney P, Parker D, Soares CB (2015) Guidance for conducting systematic scoping reviews. JBI Evid Implement 13(3):141–146 Hoy D, Brooks P, Woolf A, Blyth F, March L, Bain C, Baker P, Smith E, Buchbinder R (2012) Assessing risk of bias in prevalence studies: modification of an existing tool and evidence of interrater agreement. J Clin Epidemiol 65(9):934–939 Sen HT, Linh TT, Trang DT (2020) Factors related to treatment compliance among patients with heart failure. Ramathibodi Med J 43(2):30–40 Shah D, Simms K, Barksdale DJ, Wu JR (2015) Improving medication adherence of patients with chronic heart failure: challenges and solutions. Res Rep Clin Cardiol. :87–95 Gowani AA Level and predictors of self-care behaviors (SCB) among educated and uneducated patients with heart failure (HF) in Pakistan van der Wal MH, van Veldhuisen DJ, Veeger NJ, Rutten FH, Jaarsma T (2010) Compliance with non-pharmacological recommendations and outcome in heart failure patients. Eur Heart J 31(12):1486–1493 Silva AF, Cavalcanti AC, Malta M, Arruda CS, Gandin T, Fé AD, Rabelo-Silva ER (2015) Treatment adherence in heart failure patients followed up by nurses in two specialized clinics. Rev Latinoam Enferm 23:888–894 Manwere A, Saburi G, Charumbira A, Mukona D, Zvinavashe M The relationship between self-care practices and readmissions among adults with chronic heart failure Pallangyo P, Millinga J, Bhalia S, Mkojera Z, Misidai N, Swai HJ, Hemed NR, Kaijage A, Janabi M (2020) Medication adherence and survival among hospitalized heart failure patients in a tertiary hospital in Tanzania: a prospective cohort study. BMC Res Notes 13:1–8 Osborn CY, Kripalani S, Goggins KM, Wallston KA (2017) Financial strain is associated with medication nonadherence and worse self-rated health among cardiovascular patients. J Health Care Poor Underserved 28(1):499 Jankowska-Polańska B, Świątoniowska-Lonc N, Sławuta A, Krówczyńska D, Dudek K, Mazur G (2020) Patient-Reported Compliance in older age patients with chronic heart failure. PLoS ONE 15(4):e0231076 Patidar AB, Kaur H, Kumar R (2021) Self care of heart failure and health related quality of life among congestive heart failure patients in Punjab, India. Int J Health Sci Res 11:68–74 Riegel B, Dickson VV, Vellone E (2022) The situation-specific theory of heart failure self-care: an update on the problem, person, and environmental factors influencing heart failure self-care. J Cardiovasc Nurs 37(6):515 van Der Wal MH, Jaarsma T, Moser DK, Veeger NJ, van Gilst WH, van Veldhuisen DJ (2006) Compliance in heart failure patients: the importance of knowledge and beliefs. Eur Heart J 27(4):434–440 Gathright EC, Dolansky MA, Gunstad J, Redle JD, Josephson RA, Moore SM, Hughes JW (2017) The impact of medication nonadherence on the relationship between mortality risk and depression in heart failure. Health Psychol 36(9):839 Evangelista LS, Berg J, Dracup K (2001) Relationship between psychosocial variables and compliance in patients with heart failure. Heart Lung 30(4):294–301 Marti CN, Georgiopoulou VV, Giamouzis G, Cole RT, Deka A, Tang WW, Dunbar SB, Smith AL, Kalogeropoulos AP, Butler J (2013) Patient-Reported selective adherence to heart failure Self‐Care recommendations: a prospective cohort study: the Atlanta cardiomyopathy consortium. Congestive heart Fail 19(1):16–24 Koirala B, Himmelfarb CR, Budhathoki C, Davidson PM (2020) Heart failure self-care, factors influencing self-care and the relationship with health-related quality of life: A cross-sectional observational study. Heliyon. ;6(2) Rockwell JM, Riegel B (2001) Predictors of self-care in persons with heart failure. Heart Lung 30(1):18–25 Khaledi GH, Mostafavi F, Eslami AA, Afza HR, Akbar H (2015) Evaluation of the effect of perceived social support on promoting self-care behaviors of heart failure patients referred to the cardiovascular research center of Isfahan. Iran Red Crescent Med J. ; 17(6) Gallagher R, Luttik ML, Jaarsma T (2011) Social support and self-care in heart failure. J Cardiovasc Nurs 26(6):439–445 Ok JS, Choi H (2015) Factors affecting adherence to self-care behaviors among outpatients with heart failure in Korea. Korean J Adult Nurs 27(2):242–250 Additional Declarations The authors declare no competing interests. Supplementary Files Supplementalfile1.docx Search strategy SupplementalTable1.docx PRISMA checklist SupplementalTable2and3.docx Quality assessment of the included studies using the Joanna Briggs Institute (JBI) quality appraisal criteria for cross-sectional studies Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5490362","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":380449193,"identity":"7556bd19-ebe4-4db2-9952-d1b1d7cc57a1","order_by":0,"name":"Tigabu Munye 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interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eNoncompliance with self-care behavior and associated factors among adult patients with heart failure in Ethiopia: A systematic review and meta-analysis\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eHeart failure (HF) is a major public health concern and a leading cause of death among adults and elderly individuals [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], affecting more than 37.7\u0026nbsp;million individuals worldwide [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Nearly 1\u0026nbsp;million new cases are diagnosed annually, which is expected to increase by 46% by 2030 [\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. The majority of patients with HF are three to four times more common in Africa than in Western countries [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. In particular, in sub-Saharan Africa (SSA), HF has significant public health importance due to its greater impact on young economically active individuals, resulting in substantial disability, premature death, and loss of economic productivity [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. The rates of HF-associated deaths were greater in low- and middle-income countries (LMICs) than in high-income countries (HICs) [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. In Ethiopia, 30\u0026ndash;40% of heart failure patients die annually [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. HF costs approximately 108\u0026nbsp;billion dollars of global health-related expenditures annually, and the majority of costs are related to hospital care [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSelf-care behavior (SCB) is a \"naturalistic decision-making process\" in which individuals maintain health through health-promoting practices and managing illness [\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. HF patients who are compliant with SCB require self-medication, weight monitoring, salt restriction, fluid limitations, regular exercise, hygiene, early treatment after symptoms occur, and keeping appointments [\u003cspan additionalcitationids=\"CR15\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Compliance with SCB has a key role in improving patients\u0026rsquo; quality of life, reducing both economic and personal burdens, facilitating early detection of clinical problems, lowering mortality, and reducing the risk of rehospitalization for HF [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. The occurrence of noncompliance with SCB is becoming a major problem in developed and developing countries [\u003cspan additionalcitationids=\"CR20 CR21 CR22\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. A wide range of risk factors affect the compliance of HF patients with the recommended components of SCB. Patients\u0026rsquo; characteristics such as age, sex, marital status, religion, place of residence, educational levels, occupation, and family income, and clinical characteristics like duration of diagnosis, stage of HF, comorbidity, prior hospitalization, awareness of heart failure, presence of depressive symptoms, and social support [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan additionalcitationids=\"CR25 CR26 CR27\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAlthough several primary studies have been conducted to determine the prevalence of noncompliance with SCB among adult patients with HF in Ethiopia, these studies have reported different findings and have shown epidemiological variations ranging from 37.4% [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e] to 77.70% [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Consequently, this study aimed to determine the pooled prevalence of noncompliance with SCB and identify the associated factors among adult patients with HF in Ethiopia. The findings of this study play a key role for stakeholders and policymakers, giving strong evidence of noncompliance with SCB among HF patients to plan intervention programs.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eReporting and registration protocol\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) framework\u0026nbsp;[31]\u0026nbsp;was used to conduct this study (Supplemental Table 1).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe review protocol was registered with the Prospero database: www.crd.york.ac.uk/PROSPERO/display_record.asp?ID=CRD42023450821.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSearch strategy \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe adapted PICO format was used to retrieve the relevant studies. The PICO consists of population (P), exposure (E), context (C), and outcome (O) as detailed below.\u003c/p\u003e\n\u003col style=\"list-style-type: lower-alpha;\"\u003e\n \u003cli\u003e\u003cstrong\u003ePopulation\u003c/strong\u003e: Adult patients with HF.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eExposure\u003c/strong\u003e: Associated factors, risk factors, determinants, and predictors i.e.\u0026nbsp;age, sex, marital status, religion, place of residence, educational levels, occupation, and family income of the patients, duration of diagnosis, stage of HF, comorbidities, prior hospitalization, awareness of heart failure, presence of depressive symptoms, and social support.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eContext\u003c/strong\u003e: Ethiopia; Addis Ababa, Amhara, Oromia, southern nations, nationalities and peoples, Sidama, and Tigray regions.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eOutcome\u003c/strong\u003e: Noncompliance with SCB.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eUsing the above PICO, we developed the following review questions which focused on identifying all the relevant studies.\u003c/p\u003e\n\u003col\u003e\n \u003cli\u003eWhat is the prevalence of noncompliance with SCB among HF patients in Ethiopia?\u003c/li\u003e\n \u003cli\u003eWhat are the factors associated with noncompliance with SCB among HF patients in Ethiopia?\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eWe searched the Google Scholar search engine, Scopus, PubMed, and Web of Science databases, the University\u0026rsquo;s Research Repository Online Library, citation tracking, and manual search of reference lists for relevant studies that reported the prevalence of noncompliance with SCB and associated factors among adult patients with HF using the following MeSH terms: (ʺNoncomplianceʺ [MeSH term] OR ʺNonadherenceʺ [MeSH term] OR ʺComplianceʺ [MeSH term] OR ʺAdherenceʺ [MeSH term]) AND (ʺSelf-care behaviorʺ [MeSH term] OR ʺSelf-care practiceʺ [MeSH term]) AND (ʺPredictorsʺ [MeSH term] OR ʺAssociated factorsʺ [MeSH term] OR ʺRisk factorsʺ [MeSH term] OR ʺDeterminantsʺ [MeSH term]) AND ʺEthiopiaʺ (Supplemental file 1). Two independent authors (TMA and YMT) participated in searching for relevant studies from December 12-18/2023. The search string was developed using ʺANDʺ and ʺORʺ Boolean operators. The included studies were published in English between 2014 and 2022 in Ethiopia.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEligibility criteria\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll observational studies that were conducted among adult (aged \u0026ge;18 years )\u0026nbsp;patients with heart failure in Ethiopia\u0026nbsp;and reported the prevalence of\u0026nbsp;noncompliance to SCB\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eand/or at least one factor associated with\u0026nbsp;noncompliance with SCB among adult patients with HF\u0026nbsp;and published in English were included in the study. However, articles without abstracts and/or full texts, systematic reviews, meta-analyses, or qualitative studies were excluded from the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStudy selection\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll the accessed studies were exported to the EndNote version 7 reference manager to remove duplicate studies. First, two independent investigators (TMA and SDK) screened the titles and abstracts, followed by the full-text reviews to determine the eligibility of each study. Discrepancies between the investigators were solved through dialog.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData extraction\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe two independent reviewers (TMA and WNA) extracted the data using structured Microsoft Excel. When discrepancies were observed in the extracted data, the phase was repeated. When discrepancies between the extracted data continued, the third reviewer (SA) participated. The name of the first author, year of publication, study area, study design, sample size, response rate, and effect size of the included primary studies were extracted. Since all the included studies were cross-sectional, the study could not perform a subgroup analysis according to the study design.\u0026nbsp;Moreover, there were inconsistencies in the outcome measurement across the included studies.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOutcome measures\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study focused on two main outcome measures. The primary outcome of interest was the prevalence of\u0026nbsp;noncompliance with SCB among adult patients with HF. The second outcome was identifying factors associated with\u0026nbsp;noncompliance with SCB among adult patients with HF.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOperational definition\u003c/strong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNoncompliance with SCB is \u0026quot;an inability to promote health, prevent disease, maintain health, and cope or deal with illness and disability with or without the support of a healthcare provider\u0026quot;\u0026nbsp;[15]. Noncompliance with SCB is appreciated if less than the midpoint (50%) of the revised nine-item European Heart Failure Self-care Behavior Scale (EHFScBS-9)\u0026nbsp;[21, 32].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData analysis\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe extracted data were exported to STATA version 17 for analysis. The pooled prevalence of\u0026nbsp;noncompliance with SCB was estimated using\u0026nbsp;a random-effects DerSimonian-Laird model\u0026nbsp;[33]. A funnel plot was used to assess publication bias. Asymmetry of the funnel plot is an indicator of publication bias, and Egger\u0026rsquo;s test with a p-value of \u0026lt;0.05 was used to determine a significant publication bias\u0026nbsp;[34]. The heterogeneity across the included primary studies was assessed using I\u003csup\u003e2\u003c/sup\u003e statistics\u0026nbsp;[35]. The I\u003csup\u003e2\u003c/sup\u003e statistics ranged from 0 to 100%, and I\u003csup\u003e2\u003c/sup\u003e values of 0,\u003csup\u003e\u0026nbsp;\u003c/sup\u003e25, 50, and 75% were considered to indicate no, low, moderate, and high degrees of heterogeneity respectively\u0026nbsp;[35]. A p-value of the I\u003csup\u003e2\u003c/sup\u003e statistic \u0026lt;0.05 was used to confirm substantial heterogeneity [36, 37]. Sensitivity analysis was employed to determine the effect of a single study on the overall estimate. A forest plot was generated to estimate the influence of associated factors on the outcome variable, and the 95% CI was calculated. The adjusted odds ratio (AOR) was the measure of association in the included studies.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eSearch results\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe search strategy identified 1,296 studies from PubMed (669), Google Scholar (578), Scopus (33), Web of Science (10), manual search (1), and the University\u0026rsquo;s Research Repository Online Library (5) studies. After removing the irrelevant studies based on their titles and abstracts (n=968) and duplicated studies (n=74), a total of 254 studies were included in the full-text review.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSubsequently, full-text reviews were conducted, resulting in the removal of 203 studies because of a lack of complete texts. Then, 51 studies were assessed for full-text reviews, and 34 studies were excluded. Finally, 17 studies were found to be applicable for determining the pooled prevalence of\u0026nbsp;noncompliance with SCB\u0026nbsp;and identifying its associated factors. The PRISMA flow chart\u0026nbsp;[38]\u0026nbsp;was constructed to show the selection process (Figure 1).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCharacteristics of the included primary studies\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll 17\u0026nbsp;[10, 24-30, 39-47]\u0026nbsp;studies were cross-sectional. Concerning geographical region, five studies\u0026nbsp;[24, 26, 29, 40, 41]\u0026nbsp;were conducted in Oromia; four\u0026nbsp;[27, 30, 42, 46]\u0026nbsp;in Amhara, three\u0026nbsp;[10, 39, 45]\u0026nbsp; in Addis Ababa, two\u0026nbsp;[43, 44]\u0026nbsp; in south nations, nationalities and people, two\u0026nbsp;[28, 47]\u0026nbsp; in Sidama and one\u0026nbsp;[25]\u0026nbsp;in Tigray. \u0026nbsp;The total sample size of the included primary studies was 5,528, where the smallest (229) and the largest (424) sample sizes were obtained in the Sidama and Oromia regions respectively. The prevalence of\u0026nbsp;noncompliance with SCB\u0026nbsp;was obtained from all seventeen included primary studies\u0026nbsp;[10, 24-30, 39-47], and the data regarding associated factors\u0026nbsp;were obtained from the fifteen primary studies\u0026nbsp;[10, 24-30, 39, 41-44, 46, 47], with a response rate ranging from 95.3 to 100% (Table 1).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e: \u0026nbsp;Summary of the included primary studies, 2023\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"806\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 4.34243%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eID\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.062%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAuthor [Year]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.933%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStudy area\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.18859%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStudy design\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6551%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMeasuring tool\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.69975%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean age\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.44417%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSample size\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.2382%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePrevalence (95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.43672%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eQuality\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 4.34243%;\"\u003e\n \u003col\u003e\n \u003cli\u003e\u0026nbsp;\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.062%;\"\u003e\n \u003cp\u003eAssen M 2017 [30]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.933%;\"\u003e\n \u003cp\u003eAmhara\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.18859%;\"\u003e\n \u003cp\u003eCS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6551%;\"\u003e\n \u003cp\u003eEHFScBS-6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.69975%;\"\u003e\n \u003cp\u003e49.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.44417%;\"\u003e\n \u003cp\u003e310\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.2382%;\"\u003e\n \u003cp\u003e77.70(73.07,82.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.43672%;\"\u003e\n \u003cp\u003eLow risk\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 4.34243%;\"\u003e\n \u003col start=\"2\"\u003e\n \u003cli\u003e\u0026nbsp;\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.062%;\"\u003e\n \u003cp\u003eBaymot A 2022 [39]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.933%;\"\u003e\n \u003cp\u003eA.A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.18859%;\"\u003e\n \u003cp\u003eCS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6551%;\"\u003e\n \u003cp\u003eEHFScBS-6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.69975%;\"\u003e\n \u003cp\u003e45.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.44417%;\"\u003e\n \u003cp\u003e294\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.2382%;\"\u003e\n \u003cp\u003e67.30(61.94, 72.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.43672%;\"\u003e\n \u003cp\u003eLow risk\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 4.34243%;\"\u003e\n \u003col start=\"3\"\u003e\n \u003cli\u003e\u0026nbsp;\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.062%;\"\u003e\n \u003cp\u003eBeker J 2014 [24]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.933%;\"\u003e\n \u003cp\u003eOromia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.18859%;\"\u003e\n \u003cp\u003eCS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6551%;\"\u003e\n \u003cp\u003eEHFScBS-11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.69975%;\"\u003e\n \u003cp\u003e48.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.44417%;\"\u003e\n \u003cp\u003e255\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.2382%;\"\u003e\n \u003cp\u003e59.20(53.17, 65.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.43672%;\"\u003e\n \u003cp\u003eLow risk\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 4.34243%;\"\u003e\n \u003col start=\"4\"\u003e\n \u003cli\u003e\u0026nbsp;\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.062%;\"\u003e\n \u003cp\u003eFetensa G 2017 [40]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.933%;\"\u003e\n \u003cp\u003eOromia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.18859%;\"\u003e\n \u003cp\u003eCS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6551%;\"\u003e\n \u003cp\u003eEHFScBS-6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.69975%;\"\u003e\n \u003cp\u003e48.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.44417%;\"\u003e\n \u003cp\u003e424\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.2382%;\"\u003e\n \u003cp\u003e48.80(44.04, 53.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.43672%;\"\u003e\n \u003cp\u003eLow risk\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 4.34243%;\"\u003e\n \u003col start=\"5\"\u003e\n \u003cli\u003e\u0026nbsp;\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.062%;\"\u003e\n \u003cp\u003eGetachew A 2022 [41]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.933%;\"\u003e\n \u003cp\u003eOromia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.18859%;\"\u003e\n \u003cp\u003eCS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6551%;\"\u003e\n \u003cp\u003eEHFScBS-11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.69975%;\"\u003e\n \u003cp\u003e47.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.44417%;\"\u003e\n \u003cp\u003e420\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.2382%;\"\u003e\n \u003cp\u003e46.40(41.63, 51.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.43672%;\"\u003e\n \u003cp\u003eLow risk\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 4.34243%;\"\u003e\n \u003col start=\"6\"\u003e\n \u003cli\u003e\u0026nbsp;\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.062%;\"\u003e\n \u003cp\u003eHailu Gebru T 2021 [25]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.933%;\"\u003e\n \u003cp\u003eTigray\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.18859%;\"\u003e\n \u003cp\u003eCS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6551%;\"\u003e\n \u003cp\u003eEHFScBS-11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.69975%;\"\u003e\n \u003cp\u003e45.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.44417%;\"\u003e\n \u003cp\u003e408\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.2382%;\"\u003e\n \u003cp\u003e54.20(49.37, 59.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.43672%;\"\u003e\n \u003cp\u003eLow risk\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 4.34243%;\"\u003e\n \u003col start=\"7\"\u003e\n \u003cli\u003e\u0026nbsp;\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.062%;\"\u003e\n \u003cp\u003eMolla B 2022 [42]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.933%;\"\u003e\n \u003cp\u003eAmhara\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.18859%;\"\u003e\n \u003cp\u003eCS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6551%;\"\u003e\n \u003cp\u003eEHFScBS-6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.69975%;\"\u003e\n \u003cp\u003e55.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.44417%;\"\u003e\n \u003cp\u003e304\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.2382%;\"\u003e\n \u003cp\u003e67.10(61.82, 72.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.43672%;\"\u003e\n \u003cp\u003eLow risk\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 4.34243%;\"\u003e\n \u003col start=\"8\"\u003e\n \u003cli\u003e\u0026nbsp;\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.062%;\"\u003e\n \u003cp\u003eMulugeta T 2022 [26]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.933%;\"\u003e\n \u003cp\u003eOromia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.18859%;\"\u003e\n \u003cp\u003eCS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6551%;\"\u003e\n \u003cp\u003eEHFScBS-6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.69975%;\"\u003e\n \u003cp\u003e45.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.44417%;\"\u003e\n \u003cp\u003e266\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.2382%;\"\u003e\n \u003cp\u003e50.0(43.99, 56.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.43672%;\"\u003e\n \u003cp\u003eLow risk\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 4.34243%;\"\u003e\n \u003col start=\"9\"\u003e\n \u003cli\u003e\u0026nbsp;\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.062%;\"\u003e\n \u003cp\u003eSeid MA 2019 [27]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.933%;\"\u003e\n \u003cp\u003eAmhara\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.18859%;\"\u003e\n \u003cp\u003eCS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6551%;\"\u003e\n \u003cp\u003eEHFScBS-6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.69975%;\"\u003e\n \u003cp\u003e49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.44417%;\"\u003e\n \u003cp\u003e310\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.2382%;\"\u003e\n \u003cp\u003e77.70(73.07,\u0026nbsp;82.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.43672%;\"\u003e\n \u003cp\u003eLow risk\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 4.34243%;\"\u003e\n \u003col start=\"10\"\u003e\n \u003cli\u003e\u0026nbsp;\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.062%;\"\u003e\n \u003cp\u003eSewagegn N 2015 [29]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.933%;\"\u003e\n \u003cp\u003eOromia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.18859%;\"\u003e\n \u003cp\u003eCS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6551%;\"\u003e\n \u003cp\u003eEHFScBS-6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.69975%;\"\u003e\n \u003cp\u003e52.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.44417%;\"\u003e\n \u003cp\u003e328\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.2382%;\"\u003e\n \u003cp\u003e37.40(32.16, 42.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.43672%;\"\u003e\n \u003cp\u003eLow risk\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 4.34243%;\"\u003e\n \u003col start=\"11\"\u003e\n \u003cli\u003e\u0026nbsp;\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.062%;\"\u003e\n \u003cp\u003eShafi Surur H 2022 [43]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.933%;\"\u003e\n \u003cp\u003eSNNP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.18859%;\"\u003e\n \u003cp\u003eCS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6551%;\"\u003e\n \u003cp\u003eEHFScBS-11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.69975%;\"\u003e\n \u003cp\u003e45.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.44417%;\"\u003e\n \u003cp\u003e292\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.2382%;\"\u003e\n \u003cp\u003e64.70(59.22, 70.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.43672%;\"\u003e\n \u003cp\u003eLow risk\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 4.34243%;\"\u003e\n \u003col start=\"12\"\u003e\n \u003cli\u003e\u0026nbsp;\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.062%;\"\u003e\n \u003cp\u003eSigebo E 2022 [44]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.933%;\"\u003e\n \u003cp\u003eSNNP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.18859%;\"\u003e\n \u003cp\u003eCS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6551%;\"\u003e\n \u003cp\u003eEHFScBS-11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.69975%;\"\u003e\n \u003cp\u003eN.A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.44417%;\"\u003e\n \u003cp\u003e240\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.2382%;\"\u003e\n \u003cp\u003e58.30(52.06, 64.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.43672%;\"\u003e\n \u003cp\u003eLow risk\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 4.34243%;\"\u003e\n \u003col start=\"13\"\u003e\n \u003cli\u003e\u0026nbsp;\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.062%;\"\u003e\n \u003cp\u003eSitotaw E \u0026nbsp; \u0026nbsp; 2022 [28]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.933%;\"\u003e\n \u003cp\u003eSidama\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.18859%;\"\u003e\n \u003cp\u003eCS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6551%;\"\u003e\n \u003cp\u003eEHFScBS-9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.69975%;\"\u003e\n \u003cp\u003e49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.44417%;\"\u003e\n \u003cp\u003e229\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.2382%;\"\u003e\n \u003cp\u003e65.90(59.76, 72.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.43672%;\"\u003e\n \u003cp\u003eLow risk\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 4.34243%;\"\u003e\n \u003col start=\"14\"\u003e\n \u003cli\u003e\u0026nbsp;\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.062%;\"\u003e\n \u003cp\u003eTegegn BW 2021 [10]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.933%;\"\u003e\n \u003cp\u003eA.A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.18859%;\"\u003e\n \u003cp\u003eCS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6551%;\"\u003e\n \u003cp\u003eEHFScBS-6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.69975%;\"\u003e\n \u003cp\u003eN.A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.44417%;\"\u003e\n \u003cp\u003e396\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.2382%;\"\u003e\n \u003cp\u003e72.0(67.58, 76.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.43672%;\"\u003e\n \u003cp\u003eLow risk\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 4.34243%;\"\u003e\n \u003col start=\"15\"\u003e\n \u003cli\u003e\u0026nbsp;\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.062%;\"\u003e\n \u003cp\u003eTole S 2015 [45]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.933%;\"\u003e\n \u003cp\u003eA.A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.18859%;\"\u003e\n \u003cp\u003eCS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6551%;\"\u003e\n \u003cp\u003eEHFScBS-11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.69975%;\"\u003e\n \u003cp\u003eN.A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.44417%;\"\u003e\n \u003cp\u003e384\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.2382%;\"\u003e\n \u003cp\u003e58.0(53.06, 62.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.43672%;\"\u003e\n \u003cp\u003eLow risk\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 4.34243%;\"\u003e\n \u003col start=\"16\"\u003e\n \u003cli\u003e\u0026nbsp;\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.062%;\"\u003e\n \u003cp\u003eYazew KG 2017 [46]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.933%;\"\u003e\n \u003cp\u003eAmhara\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.18859%;\"\u003e\n \u003cp\u003eCS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6551%;\"\u003e\n \u003cp\u003eEHFScBS-11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.69975%;\"\u003e\n \u003cp\u003e52.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.44417%;\"\u003e\n \u003cp\u003e403\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.2382%;\"\u003e\n \u003cp\u003e62.30(57.57, 67.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.43672%;\"\u003e\n \u003cp\u003eLow risk\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 4.34243%;\"\u003e\n \u003col start=\"17\"\u003e\n \u003cli\u003e\u0026nbsp;\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.062%;\"\u003e\n \u003cp\u003eZewdu Agegnehu 2022 [47]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.933%;\"\u003e\n \u003cp\u003eSidama\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.18859%;\"\u003e\n \u003cp\u003eCS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6551%;\"\u003e\n \u003cp\u003eEHFScBS-11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.69975%;\"\u003e\n \u003cp\u003eN.A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.44417%;\"\u003e\n \u003cp\u003e265\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.2382%;\"\u003e\n \u003cp\u003e47.30(41.29, 53.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.43672%;\"\u003e\n \u003cp\u003eLow risk\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cem\u003eAbbreviations: AA, Addis Ababa; CS, cross-sectional;\u003c/em\u003e \u003cem\u003eEHFScBS, European Heart Failure Self-care Behavior Scale\u003c/em\u003e; \u003cem\u003eN.A\u003c/em\u003e, \u003cem\u003enot available\u003c/em\u003e;\u003cem\u003e\u0026nbsp;\u003c/em\u003eSNNP\u003cem\u003e, southern nations, nationalities and peoples.\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eQuality\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eassessment\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eof the included primary studies\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTwo independent authors (TMA and SDK) assessed the quality of the included primary studies. The quality of each study was appraised using\u0026nbsp;the Joanna Briggs Institute (JBI) quality assessment criteria\u0026nbsp;[48]. All the included 17 primary studies\u0026nbsp;[10, 24-30, 39-47]\u0026nbsp;\u0026nbsp;were assessed using the JBI checklist for a cross-sectional study. Of the 17 studies, twelve scored seven of the eight questions, 87.5% (low risk), three scored six of the eight questions, 75% (low risk), and the remaining two scored five of the eight questions, 62.5% (low risk) (Supplemental Table 2).\u003c/p\u003e\n\u003cp\u003eStudies were considered to be of low risk when they scored 50% or higher on the quality assessment indicators.\u0026nbsp;The studies scored between 5 and 7 out of a total of 8 points. Thus, all the included primary studies\u0026nbsp;[10, 24-30, 39-47]\u0026nbsp;\u0026nbsp;were of good quality.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRisk of bias assessment of the included\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eprimary\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;studies\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe standardized assessment tool\u0026nbsp;[49]\u0026nbsp;was used to evaluate the risk of bias. Accordingly, of the total of the seventeen included studies, fourteen scored eight of the ten questions and three studies scored seven of the ten questions. Studies were classified as ʺlow riskʺ if eight and above of the ten questions received ʺYesʺ, as ʺmoderate riskʺ if six to seven of the ten questions received ʺYesʺ and as ʺhigh riskʺ if five or lower of the ten questions received ʺYesʺ.\u0026nbsp;As a result, all the included primary studies\u0026nbsp;[10, 24-30, 39-47]\u0026nbsp; had a low risk of bias (good quality)\u0026nbsp;(Supplemental Table 3).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePooled prevalence of noncompliance with self-care behavior\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 17 eligible primary studies\u0026nbsp;[10, 24-30, 39-47]\u0026nbsp; with 5,528 participants were included in the study. In this meta-analysis, the pooled prevalence of\u0026nbsp;noncompliance with SCB among adult patients with HF\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003ewas 59.70% (95% CI: 54.08, 65.32); I\u003csup\u003e2\u003c/sup\u003e=95.00%; P\u0026lt;0.001) (Figure 2).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePublication bias\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe funnel plot showed a symmetrical distribution (Figure 3), and the p-value of Egger\u0026apos;s test was\u0026nbsp;0.2976,\u0026nbsp;which indicated the absence of publication bias in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInvestigation of heterogeneity\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe percentage of I\u003csup\u003e2\u003c/sup\u003e statistics\u0026nbsp;in the forest plot showed significant heterogeneity among the included primary studies\u0026nbsp;(I\u003csup\u003e2\u003c/sup\u003e=95.00%; P\u0026lt;0.001) (Figure 2).\u0026nbsp;Therefore,\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003esensitivity and\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003esubgroup analyses were performed to manage the heterogeneity.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSensitivity analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA sensitivity analysis was also conducted to check for the presence of outliers in the included primary studies. The forest plot revealed that the estimate of a single study was closer to the pooled estimate, indicating the absence of a particular study effect on the overall pooled estimate\u0026nbsp;(Figure 4).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSubgroup analysis\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe subgroup analysis was performed based on the study area and measuring tools used. Hence, the highest pooled prevalence of noncompliance with SCB was among those studies conducted in Amhara [71.24, 95% CI: 63.51, 78.96; I\u003csup\u003e2\u003c/sup\u003e=90.30%, P\u0026lt;0.001], and the lowest pooled prevalence was among those studies conducted in Oromia region [48.25, 95% CI: 41.76, 54.74; I\u003csup\u003e2\u003c/sup\u003e=86.61%; P\u0026lt;0.001]. Similarly, the higher pooled prevalence of noncompliance with SCB was among those studies that used a measuring tool of 6-items of European Heart Failure Self-care Behavior Scale (EHFScBS-6) [62.29, 95%CI: 51.93, 72.65; I\u003csup\u003e2\u003c/sup\u003e=97.10%; P\u0026lt;0.001] followed by those studies that used a measuring tool of 11-items of EHFScBS (EHFScBS-11) \u0026nbsp;[56.29, 95% CI: 51.64, 60.94; I\u003csup\u003e2\u003c/sup\u003e=83.65%; P\u0026lt;0.001]\u0026nbsp;(Table 2).\u0026nbsp;Therefore, the heterogeneity of the study could be because of differences in the study area and measuring tools used.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2\u003c/strong\u003e: Subgroup analyses of noncompliance with self-care behavior\u0026nbsp;among adult patients with heart failure\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003ein Ethiopia, 2023\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"789\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003eOutcome\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003eSubgroup\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eNo. of studies\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003eModel\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003ePrevalence\u003c/p\u003e\n \u003cp\u003e(95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003eI\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"6\" valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003eStudy area\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"6\" valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003eNoncompliance with SCB\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003eA.A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003eRandom\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003e65.81(57.48, 74.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e88.49%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003eAmhara\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003eRandom\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003e71.24 (63.51, 78.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e90.30%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003eOromia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003eRandom\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003e48.25 (41.76, 54.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e86.61%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003eSNNP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003eRandom\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003e61.68 (55.42, 67.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e56.17%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026lt;0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003eSidama\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003eRandom\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003e56.59 (38.36, 74.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e94.44%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003eTigray\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003eRandom\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003e54.20 (49.37, 59.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.00%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026lt;1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003eMeasuring tool\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003eNoncompliance with SCB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003eEHFScBS-6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003eRandom\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003e62.29 (51.93, 72.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e97.10%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003eEHFScBS-11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003eRandom\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003e56.29 (51.64, 60.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e83.65%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003eEHFScBS-9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003eRandom\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003e65.90 (59.76, 72.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026lt;1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cem\u003eNote: A.A, Addis Ababa; CI, confidence interval;\u003c/em\u003e \u003cem\u003eEHFScBS, European Heart Failure Self-care Behavior Scale;\u003c/em\u003e\u003cem\u003e\u0026nbsp;SNNP, southern nations, nationalities and peoples\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFactors associated with\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003enoncompliance with self-care behavior\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn this study, eleven studies\u0026nbsp;[10, 24, 25, 27-30, 39, 43, 44, 47]\u0026nbsp;showed a significant association between comorbidities and noncompliance with SCB. The pooled AOR of noncompliance with SCB for HF patients with comorbidities was 2.40 (95% CI:\u0026nbsp;1.88, 3.07; I\u003csup\u003e2\u003c/sup\u003e=25.89%; P\u0026lt; 0.20) (Figure 5).\u003c/p\u003e\n\u003cp\u003eSix studies\u0026nbsp;[10, 24, 41-43, 46]\u0026nbsp;reported that depression was significantly associated with noncompliance with SCB. The pooled AOR of noncompliance with SCB for patients with depression was\u0026nbsp;2.70 (95% CI: 1.76, 4.14; I\u003csup\u003e2\u003c/sup\u003e=60.89%; P\u0026lt;0.03) (Figure 6).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTen studies\u0026nbsp;[10, 25-28, 39, 41, 42, 44, 46]\u0026nbsp;showed a significant association between the educational level (primary school or lower) and\u0026nbsp;noncompliance with SCB. The pooled AOR of noncompliance with SCB for patients with\u0026nbsp;educational levels (primary school or lower) was 1.72 (95% CI: 1.28, 2.30; I\u003csup\u003e2\u003c/sup\u003e=24.73%; P\u0026lt;0.22) (Figure 7).\u003c/p\u003e\n\u003cp\u003eSeven studies\u0026nbsp;[25, 39, 41, 42, 44, 46, 47]\u0026nbsp;also reported that poor social support was significantly associated with\u0026nbsp;noncompliance with SCB. The pooled AOR of noncompliance with SCB for patients who had poor social support was 2.77\u0026nbsp;(95% CI: 1.53, 5.03; I\u003csup\u003e2\u003c/sup\u003e=77.08%; P\u0026lt;0.001) (Figure 8).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe findings of this study indicated that the pooled prevalence of\u0026nbsp;noncompliance with SCB among patients with HF\u0026nbsp;was\u0026nbsp;59.70% (95% CI: 54.08, 65.32); I\u003csup\u003e2\u003c/sup\u003e=95.00%; P\u0026lt;0.001).\u0026nbsp;The findings of\u0026nbsp;this study also reported that comorbidities, depression, educational level (primary school or lower), and poor social support were significantly associated with\u0026nbsp;noncompliance with SCB among patients with HF.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn this study, the pooled prevalence of\u0026nbsp;noncompliance with SCB\u0026nbsp;was\u0026nbsp;59.70%.\u0026nbsp;This study finding\u0026nbsp;was congruent with those of studies conducted in Brazil (63.5%)\u0026nbsp;[50]\u0026nbsp;and the United States of America (USA) (64.4%)\u0026nbsp;[51]. However, the finding of this study was higher than those studies conducted in Pakistan (43.5%)\u0026nbsp;[52], Vietnam (45.5%)\u0026nbsp;[53], Kenya (50.8%)\u0026nbsp;[20], the Netherlands (52%)\u0026nbsp;[54]\u0026nbsp;and Zimbabwe (53.8%)\u0026nbsp;[55]. Conversely,\u0026nbsp;this study finding\u0026nbsp;was lower than those of studies conducted in Sudan (72%)\u0026nbsp;[56], Iran (74%)\u0026nbsp;[57], Tanzania (74.7%)\u0026nbsp;[58]\u0026nbsp;and India (77%)\u0026nbsp;[59]. These discrepancies might be due to differences in\u0026nbsp;socioeconomic factors,\u0026nbsp;healthcare characteristics, and knowledge levels\u0026nbsp;across the study populations\u0026nbsp;[39, 41, 59].\u003c/p\u003e\n\u003cp\u003eFurthermore,\u0026nbsp;the findings of the study showed that adult HF patients with comorbidities were 2.40 times more likely to develop\u0026nbsp;noncompliance with SCB than patients without comorbidities.\u0026nbsp;The finding of this study was consistent with those of studies conducted in the USA\u0026nbsp;[60]\u0026nbsp;and the Netherlands\u0026nbsp;[53]. This might be because patients who have comorbid illnesses may take various medications, and physical and disease severity may affect their ability to perform the\u0026nbsp;recommended self-care behaviors\u0026nbsp;[30].\u003c/p\u003e\n\u003cp\u003eThe findings of this study also revealed that HF patients with depression were 2.70 fold more likely to encounter\u0026nbsp;noncompliance with SCB than were their counterparts.\u0026nbsp;The finding of this study was congruent with those of studies conducted in the Netherlands\u0026nbsp;[61]\u0026nbsp;and the USA\u0026nbsp;[62]. The possible reason could be patients with depressive symptoms usually develop altered thinking abilities and unfavorable attitude toward health maintenance and become more noncompliant with SCB recommendations\u0026nbsp;[63].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSimilarly, the findings of this study showed that HF patients with educational levels of primary school or lower were 1.72 times more likely to experience\u0026nbsp;noncompliance with SCB than were those with an educational level of\u0026nbsp;secondary school or above. The finding of this study was similar with those of studies conducted in Atlanta, USA\u0026nbsp;[64]\u0026nbsp;and Nepal\u0026nbsp;[65]. \u0026nbsp;This can be explained by individuals with lower educational levels possibly having a lower level of reasoning and decision-making to perform self-care behaviors, leading to noncompliance with SCBs\u0026nbsp;[42, 66].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSimilarly, the findings of this study indicated that HF patients with poor social support were 2.77 times more likely to face\u0026nbsp;noncompliance with SCB than\u0026nbsp;patients with good social support. The finding of this study was in line with those of studies conducted in Iran\u0026nbsp;[67], the Netherlands\u0026nbsp;[68], and Korea\u0026nbsp;[69]. Good social support may act as a gentle guiding force that encourages behavioral change for better self-care behavior. Therefore, patients with poor social support cannot be encouraged to engage in better SCB\u0026nbsp;[41, 42].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStrengths and limitations of the study\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo the best of our knowledge, this systematic review and meta-analysis was the first study to pool the results of several primary studies conducted in Ethiopia, providing stronger evidence of noncompliance with SCB among HF patients. Although all the included studies were of good quality, all the included studies were cross-sectional. Moreover, there were inconsistencies in the outcome measurement across the included studies. Additionally, the study could not perform a subgroup analysis according to the study design.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe pooled prevalence of noncompliance with SCB among adult patients with HF was substantially high in Ethiopia. The review revealed that comorbidities, depression, educational level of primary school or lower, and poor social support were the pooled independent predictors of noncompliance with SCB among adult patients with HF in Ethiopia. Therefore, special attention should be given to patients with these identified risk factors. \u0026nbsp;Moreover, healthcare providers should encourage HF patients to comply with all the recommended components of SCB.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAOR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAdjusted odds ratio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eConfidence interval\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eEHFScBS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eEuropean Heart Failure Self-care Behavior Scale\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHF\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHeart failure\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHICs\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHigh-income countries\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eJBI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eJoanna Briggs Institute\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLMICs\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLow- and middle-income countries\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePRISMA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePreferred Reporting Items for Systematic Reviews and Meta-Analyses\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSCB\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSelf-care behavior\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSSA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSub-Saharan Africa\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eUSA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eUnited States of America\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTMA has generated the idea for the review. BMB, SDK, WNA, SA, SZ, and AK were involved in data collection and statistical analysis. TMA wrote the first draft of the manuscript. GL, BMM, NE, and YMT revised the manuscript. All the authors were responsible for the accuracy of the analysis and the contents of the study. Finally, the authors read and approved the final version of the manuscript for publication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgment\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to extend our gratitude to Mr. Henok Andualem for his unreserved support throughout the study.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll the necessary data and materials were included in the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll the authors declare that they have no conflicts of interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBenjamin EJ, Virani SS, Callaway CW, Chamberlain AM, Chang AR, Cheng S, Chiuve SE, Cushman M, Delling FN, Deo R, De Ferranti SD (2018) Heart disease and stroke statistics-2018 update: a report from the American Heart Association. 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J Cardiovasc Nurs 23(3):205\u0026ndash;218\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGowani A, Gul R, Dhakam S (2017) Self-care and its predictors among patients with heart failure in Karachi, Pakistan. Br J Cardiac Nurs 12(7):333\u0026ndash;338\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNg'ang\u0026rsquo;a-Oginga IH Heart failure knowledge and self care behaviour practices among ambulatory heart failure patients at Kenyatta National Hospital (Doctoral dissertation, University of Nairobi)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePobrotyn P, Mazur G, Kałużna-Oleksy M, Uchmanowicz B, Lomper K The level of self-care among patients with chronic heart failure. InHealthcare 2021 (Vol. 9, No. 9, p. 1179). MDPI\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSiabani S, Driscoll T, Davidson PM, Najafi F, Jenkins MC, Leeder SR (2016) Self-care and its predictors in patients with chronic heart failure in western Iran. 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Int J Health Sci Res 11:68\u0026ndash;74\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRiegel B, Dickson VV, Vellone E (2022) The situation-specific theory of heart failure self-care: an update on the problem, person, and environmental factors influencing heart failure self-care. J Cardiovasc Nurs 37(6):515\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003evan Der Wal MH, Jaarsma T, Moser DK, Veeger NJ, van Gilst WH, van Veldhuisen DJ (2006) Compliance in heart failure patients: the importance of knowledge and beliefs. Eur Heart J 27(4):434\u0026ndash;440\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGathright EC, Dolansky MA, Gunstad J, Redle JD, Josephson RA, Moore SM, Hughes JW (2017) The impact of medication nonadherence on the relationship between mortality risk and depression in heart failure. Health Psychol 36(9):839\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEvangelista LS, Berg J, Dracup K (2001) Relationship between psychosocial variables and compliance in patients with heart failure. Heart Lung 30(4):294\u0026ndash;301\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMarti CN, Georgiopoulou VV, Giamouzis G, Cole RT, Deka A, Tang WW, Dunbar SB, Smith AL, Kalogeropoulos AP, Butler J (2013) Patient-Reported selective adherence to heart failure Self‐Care recommendations: a prospective cohort study: the Atlanta cardiomyopathy consortium. Congestive heart Fail 19(1):16\u0026ndash;24\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKoirala B, Himmelfarb CR, Budhathoki C, Davidson PM (2020) Heart failure self-care, factors influencing self-care and the relationship with health-related quality of life: A cross-sectional observational study. Heliyon. ;6(2)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRockwell JM, Riegel B (2001) Predictors of self-care in persons with heart failure. Heart Lung 30(1):18\u0026ndash;25\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKhaledi GH, Mostafavi F, Eslami AA, Afza HR, Akbar H (2015) Evaluation of the effect of perceived social support on promoting self-care behaviors of heart failure patients referred to the cardiovascular research center of Isfahan. Iran Red Crescent Med J. ; 17(6)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGallagher R, Luttik ML, Jaarsma T (2011) Social support and self-care in heart failure. J Cardiovasc Nurs 26(6):439\u0026ndash;445\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOk JS, Choi H (2015) Factors affecting adherence to self-care behaviors among outpatients with heart failure in Korea. Korean J Adult Nurs 27(2):242\u0026ndash;250\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Debre Tabor University","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Noncompliance, Self-care, Heart failure, Ethiopia, Meta-analysis","lastPublishedDoi":"10.21203/rs.3.rs-5490362/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5490362/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e \u003cb\u003eIntroduction\u003c/b\u003e: Heart failure is a major public health concern and a leading cause of death among adults and elderly individuals worldwide. In Ethiopia, more than one-third of heart failure patients die annually. Although several primary studies have been conducted to determine the prevalence of noncompliance with self-care behavior among adult patients with heart failure in Ethiopia, these studies have reported different findings. Consequently, this study aimed to determine the pooled prevalence of noncompliance with self-care behavior among patients with heart failure in Ethiopia.\u003c/p\u003e \u003cp\u003e \u003cb\u003eMethods\u003c/b\u003e: Searches were performed on the Google Scholar search engine, Scopus, PubMed, and Web of Science databases for relevant studies. A random-effects DerSimonian-Laird model was used to calculate the pooled prevalence of noncompliance with self-care behavior. Sensitivity and subgroup analyses were performed to control marked heterogeneity, and a funnel plot was used to assess publication bias.\u003c/p\u003e \u003cp\u003e \u003cb\u003eResults\u003c/b\u003e: A total of 17 primary studies with a sample size of 5,528 participants were included in the final meta-analysis. The pooled prevalence of noncompliance with self-care behavior among adult patients with heart failure was 59.70% (95% CI: 54.08, 65.32); I\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;95.00%; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Comorbidities [AOR\u0026thinsp;=\u0026thinsp;2.40, 95% CI: 1.88, 3.07]; depression [AOR\u0026thinsp;=\u0026thinsp;2.70, 95% CI: 1.76, 4.14]; educational level (primary school or lower) [AOR\u0026thinsp;=\u0026thinsp;1.72, 95% CI: 1.28, 2.30]; and poor social support [AOR\u0026thinsp;=\u0026thinsp;2.77, 95% CI: 1.53, 5.03] were significantly associated with noncompliance with self-care behavior.\u003c/p\u003e \u003cp\u003e \u003cb\u003eConclusions\u003c/b\u003e: The pooled prevalence of noncompliance with self-care behavior among heart failure patients was considerably high in Ethiopia. Comorbidities, depression, educational level (primary school or lower), and poor social support were the pooled independent predictors of noncompliance with self-care behavior in Ethiopia. Therefore, special attention should be given to patients with these identified risk factors. Moreover, healthcare providers should encourage heart failure patients to comply with all the recommended components of self-care behavior.\u003c/p\u003e","manuscriptTitle":"Noncompliance with self-care behavior and associated factors among adult patients with heart failure in Ethiopia: A systematic review and meta-analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-11-25 15:29:53","doi":"10.21203/rs.3.rs-5490362/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"bd3329c5-e6f0-4905-8750-f5d2cd114a0f","owner":[],"postedDate":"November 25th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":40516089,"name":"Cardiac \u0026 Cardiovascular Systems"}],"tags":[],"updatedAt":"2024-11-25T15:29:54+00:00","versionOfRecord":[],"versionCreatedAt":"2024-11-25 15:29:53","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5490362","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5490362","identity":"rs-5490362","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}