Influencing factors of psychological insulin resistance among patients with type 2 diabetes: a systematic review and meta-analysis

Abstract

Background

Diabetes, particularly type 2 diabetes, is a significant global health issue, with insulin therapy being crucial for glycemic control. Psychological insulin resistance (PIR) often hinders effective treatment, impacting treatment outcomes and patient quality of life.

Objective

This systematic review and meta-analysis aimed to comprehensively analyze the influencing factors of PIR among patients with type 2 diabetes, providing insights for developing targeted interventions to enhance insulin therapy acceptance and improve diabetes management.

Methods

Following PRISMA guidelines, we conducted a systematic search across multiple databases, including PubMed, the Cochrane library, CINAHL, Embase, APA PsycInfo, Web of Science, China National Knowledge Infrastructure (CNKI), Wanfang Database, China Science and Technology Journal Database (VIP), and Sinomed, from inception to July 2025. Studies were included if they focused on type 2 diabetes patients and PIR, with exclusion of non-original research and republished literature. Quality assessment was performed using Newcastle-Ottawa Scale for cohort and case-control studies, and AHRQ standards for cross-sectional studies.

Results

A total of 22 studies involving 5,965 patients were included. The prevalence of PIR ranged from 27.1 to 82.9%. Meta-analysis indicated that female gender (OR = 0.38, 95% CI: 0.20–0.72, P = 0.003), lack of diabetes education (OR = 1.85, 95% CI: 1.37–2.49, P < 0.0001), and negative attitudes toward insulin (OR = 1.36, 95% CI: 1.17–1.57, P < 0.0001) were significant predictors of PIR. Factors influencing PIR were categorized into five themes: patient characteristics, treatment history, patient attitudes, lifestyle and economic factors, and social-psychological factors.

Conclusions

Understanding the multifaceted nature of PIR is essential for improving glycemic control and patient outcomes. This review provides valuable insights for healthcare providers and researchers, guiding the development of tailored interventions to address PIR and enhance diabetes management.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40200-025-01714-5.

Introduction

Diabetes, a chronic metabolic disorder with a growing global prevalence, represents a substantial public health challenge. Data from the International Diabetes Federation (IDF) indicate that an estimated 463 million adults worldwide were affected by diabetes in 2019, with projections suggesting an increase to 700 million by 2045 [1]. In China, the prevalence is similarly alarming, with over 100 million adult cases reported in 2019. Additionally, nearly 500 million individuals are identified as high-risk for future onset, accompanied by a concerning trend of decreasing age at diagnosis [1, 2].

Insulin, a pivotal therapeutic agent for glycemic regulation, occupies a central role in diabetes Management. For individuals with type 1 diabetes, characterized by impaired pancreatic beta-cell function and insufficient endogenous insulin production, exogenous insulin is indispensable for maintaining glycemic stability [3]. In type 2 diabetes, insulin therapy becomes imperative as the disease advances, pancreatic function deteriorates, or when glycemic control is unattainable through lifestyle modifications and oral hypoglycemic agents [4]. Similarly, in gestational diabetes, insulin is essential to ensure maternal and fetal health by maintaining blood glucose within a safe range [5]. Insulin facilitates glucose uptake by cells, suppresses hepatic gluconeogenesis, regulates glycemic homeostasis, and demonstrates anti-inflammatory and cellular repair properties, thereby playing a critical role in mitigating the onset and progression of diabetic complications [6].

However, while the introduction of insulin in the Management of Type 2 diabetes may be advantageous when glycaemic control cannot be achieved with non-insulin glucose-lowering agents, many people are reluctant to transition to insulin therapy, which is known as psychological insulin resistance (PIR). PIR refers to a diabetes management obstacle influenced by psychological factors (cognitive, emotional, relational, and cultural) and not as a psychological disorder [7]. Unlike the insulin resistance syndrome, also known as metabolic syndrome (MS), that occurs clinically due to insufficient metabolic function of the body, PIR is mainly caused by psychological factors and does not involve any organic defects [8, 9]. Howerver, this psychological barrier also delays the initiation of insulin therapy, compromises glycemic control, and elevates the risk of diabetic complications, contributing significantly to the suboptimal achievement of diabetes treatment goals [7].

Multiple studies have shown that the prevalence rate of type 2 diabetes is generally high, such as PIR rate was reported as approximately 30% in America [10], and 35.7% in Korean [11] seriously affect the blood sugar control effect of patients with type 2 diabetes and increased the risk of complication. PIR is mainly caused by emotional (e.g. anxiety about the expected impact on daily life); cognitive (e.g. distorted beliefs about insulin treatment); behavioural (e.g. unpleasant negative consequences); social (e.g. feeling stigmatised); and relational (influencing factors from the medical health team) [8]. Investigating the determinants of PIR is essential for enhancing treatment adherence, improving glycemic outcomes, and reducing complication rates in diabetic patients. Such insights can inform the optimization of diabetes management strategies, enabling healthcare providers to better understand patient psychology and implement targeted interventions, ultimately improving patients’ quality of life and long-term prognosis [12].

This study aims to conduct a systematic and comprehensive analysis of the factors influencing PIR. By synthesizing and evaluating existing research, we seek to elucidate the multifaceted causes underlying patients’ psychological resistance to insulin therapy, including individual characteristics, disease awareness, psychological state, social support, and other contributing elements. The findings will provide a robust foundation for developing tailored clinical interventions, thereby enhancing diabetic patients’ acceptance of and adherence to insulin therapy, optimizing glycemic control, and improving overall health outcomes and quality of life.

Methods

This systematic review and meta-analysis was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines [13]. The study protocol is registered in the PROSPERO database (registration ID: CRD42024609030).

Data sources

This study conducted an extensive and systematic search across multiple electronic databases, including PubMed, APA PsycInfo, the Cochrane library, CINAHL, Embase, Sinomed, Web of Science, China National Knowledge Infrastructure (CNKI), Wanfang Data Knowledge Service Platform, and VIP Chinese Sci-tech Periodical Database, to ensure the breadth and comprehensiveness of the Literature sources. The search timeframe spanned from the inception of each database to July 2025, aiming to incorporate the maximal number of relevant research findings. Search terms were (“Psychological insulin resistance”) AND (“Type 2 Diabetes Mellitus”) AND (“Factor*”). The PubMed search strategy is included in the Multimedia Appendix 1.

Inclusion and exclusion criteria

Inclusion Criteria: (1) The study population is explicitly Limited to patients with type 2 diabetes, and the research content pertains to psychological insulin resistance. (2) The investigation is dedicated to identifying factors influencing psychological insulin resistance, with consideration given to various dimensions such as disease-related cognition, psychological and emotional states, social support, and individual attributes. (3) The study design encompasses robust empirical research methods, including cohort studies, case-control designs, and cross-sectional analyses. (4) The inclusion of literature is restricted to works written in Chinese or English to facilitate comprehensive access and accurate interpretation of the materials.

Exclusion Criteria: (1) Republished literature. (2) Non-original research, including reviews, commentaries, and conference abstracts. (3) Research with incomplete or exceptionally poor-quality data, precluding the extraction of meaningful information. (4) Works whose subjects are not patients with diabetes or those that diverge significantly from the topic of psychological insulin resistance.

A total of 2071 articles were initially retrieved and subject to screening according to the established criteria. Following this process, 22 articles were identified as meeting the inclusion criteria and were consequently selected for inclusion in this review (Fig. 1). Discrepancies in selection were resolved through collaborative discussions within the research team.

Fig. 1.

Forest plot of the impact of gender on psychological insulin resistance in diabetic patients

Data extraction and quality assessment

Two researchers (HZ and ZY) independently extracted data from the included studies, documenting the title, authorship, country of research, year of publication, single- or multi-center status, funding sources, research methodology (cross-sectional, cohort, or case-control study), sample size, scales used to assess psychological insulin resistance, identified influencing factors, and corresponding effect sizes (OR and 95%CI).

Cohort and case-control studies are evaluated using the Newcastle-Ottawa Scale (NOS) [14], where scoring criteria include the selection of study subjects, comparability of groups, and measurement of exposure or outcomes. In assessing a cohort study that examines the association between diabetes progression and psychological insulin resistance, factors such as the representativeness of the included patients, the clear definition of the exposure factor (disease progression) and outcome indicator (degree of psychological insulin resistance), and the comprehensive consideration and adjustment of potential confounding variables, including age, gender, and educational, are critically examined to determine the study’s quality. A score of 9 is the full Mark. Any score below 4 is considered of low quality and excluded.

Cross-sectional studies are assessed in accordance with the quality evaluation standards recommended by the Agency for Healthcare Research and Quality (AHRQ) [15], with a thorough review of each element, including the research objective, sample selection, data collection procedures, variable measurement, and statistical analysis. For instance, when examining the relationship between diabetes patients’ disease cognition and psychological insulin resistance in a cross-sectional study, the diversity of the sample is evaluated to ensure that it encompasses patients from multiple centers and across various stages of illness; the reliability and validity of the disease cognition questionnaire are assessed to guarantee measurement accuracy; and the suitability of the statistical analysis methods is evaluated to ascertain their ability to reasonably demonstrate associations between variables. A score of 11 is the full Mark. Any score below 4 is considered of low quality and excluded.

Statistical analysis

Statistical analyses were performed using Review Manager (version 5.4). Heterogeneity among the included studies was evaluated with the I² statistic. A fixed-effects model was applied when no significant heterogeneity was detected (p > 0.1 and I² < 50%); otherwise, a random-effects model was used. Sensitivity analysis was conducted by sequentially excluding individual studies to assess the stability of the results. For dichotomous data, pooled odds ratios (ORs) and their corresponding 95% confidence intervals (CIs) were calculated. A p-value of less than 0.05 was considered statistically significant.

Results

Table 1 presents the characteristics of the 22 included studies [16–37], all of which were published between 2010 and 2025. The reported prevalence rate of PIR ranged from 27.1 to 82.9%. 21 studies provided information on geographic Location, including 12(54.5%) from China, 2(9.1%) from Saudi Arabia, 1(4.5%) from the United States, 1(4.5%) from Iraq, 1(4.5%) from Kenya, 1(4.5%) from South Korea, 1(4.5%) from South African, 1(4.5%) from Botswana and 1(4.5%) from the Republic of Congo. The sample sizes varied across these studies, with 10 (45.4%) involving fewer than 200 participants. Additionally, 6 studies (27.3%) had more than 400 participants, while 6 (27.3%) had participant counts ranging between 200 and 400. Of the 22 studies included, 21(95.5%) were cross-sectional investigations. Among these, 11 studies achieved an AHRQ score of ≥ 7, 10 studies scored between 4 and 6. Only 1 were case-control studies with NOS scores of 6. The results from the literature quality assessment are detailed in the Multimedia Appendix 2.

Table 1.

Characteristics of the reviewed studies (N = 22)

	Year	Author	Country	Study design	Participant information				PIR rate	PIR measure	NOS-AHRQ
					sample size	Male(%)	Female(%)	Age(Mean ± SD)
1	2010	Xiaoying Ding et al. [16]	China	Cross-sectional study	559	316(67.2%)	154(32.8%)	-	-	Insulin treatment attitude scale	8
2	2011	Yunxia Gao et al. [17]	China	Cross-sectional study	96	43(44.8%)	53(55.2%)	56.7 ± 12.0	27.10%	BIT	5
3	2016	Ling Zhou et al. [18]	China	Cross-sectional study	100	58(58%)	32(32%)	-	58%	Insulin treatment attitude scale	7
4	2022	Jing Chen et al. [19]	China	Cross-sectional study	470	316(67.3%)	154(32.7%)	54.83 ± 13.63	-	MOI	7
5	2020	Weiwei Xing et al. [20]	China	Cross-sectional study	161	89 (52.4%)	81 (47.6%)	59.21 ± 13.01	-	ITAS	7
6	2019	Haiyan Li et al. [21]	China	Cross-sectional study	188	107(56.9%)	81(43.1%)	-	-	MOI	7
7	2014	Xiumei Ni et al. [22]	China	Cross-sectional study	137	-	-	-	-	MOI	6
8	2024	Shilong Zhang et al. [23]	China	Cross-sectional study	660	219(33.2%)	441(66.8%)	-	82.10%	MOI	6
9	2019	Shomba L Rita et al. [24]	Kinshasa, Democratic Republic of Congo	Cross-sectional study	213	84(39.4%)	129(60.6)	59.8 ± 11.1	42.70%	Chinese Attitudes to Starting Insulin Questionnaire (Ch-ASIQ)	6
10	2020	Patrick Ngassa Piotie et al. [25]	South African Tshwane	Cross-sectional study	468	157 (33.5%)	311 (66.5%)	57.2 ± 11.3	52%	Self-made scales	7
11	2016	Mohammed Ali Batais et al. [26]	Saudi Arabia	Cross-sectional study	408	214(52.5%)	194(47.5%)	-	34.60%	Self-made scales	8
12	2020	Asmaa M Alomrani et al. [27]	Saudi Arabia	Cross-sectional study	366	188 (51.4%)	178 (48.6%)	47.71 ± 11.33	32.50%	-	4
13	2010	Soohyun Nam et al. [28]	United States of America	Cross-sectional study	178	82 (46.1%)	96 (53.9%)	64.3 ± 13.54	-	BIT	5
14	2017	Asif H Gulam et al. [29]	Kenya	Cross-sectional study	167	68(40.7%)	99(59.3%)	55.5 ± 13.8	82.60%	ITAS	8
15	2019	Ji Hyeon Yu et al. [30]	South Korea	Cross-sectional study	136	86(63.2%)	50 (36.8%)	58.34	-	Psychological Insulin Resistance Scale	8
16	2015	Kam Pui Lee et al. [31]	Hong Kong, China	Cross-sectional study	402	158(39.2%)	244(60.8%)	-	-	ITAS	5
17	2025	Muhammad, H J [32]	Iraq	Cross-sectional study	111	81(72.97%)	30(27.03%)	56.34 ± 14.43	55.9%	self-made scale	6
18	2025	Bitsang, E [33]	Botswana	Cross-sectional study	228	92(40.4%)	136(59.6%)	-	82.9%	ITAS	7
19	2024	Xiaoxue Han [34]	China	Cross-sectional study	204	114(55.9%)	90(44.1%)	65.71 ± 10.91	50.98%	ITAS	6
20	2024	Kairui Dong [35]	China	Cross-sectional study	324	167(51.5%)	157(48.5%)	-	-	ITAS	5
21	2024	Rongrong Wu [36]	China	Cross-sectional study	289	178(61.6%)	111(38.4%)	51.53 ± 12.61	-	ITAS	7
22	2014	Maha M. El Shafei et al. [37]	-	Case-control study	100	38(38%)	62(62%)	-	40%	ITAS	6

Abbreviations: PIR = Psychological insulin resistance, NOS = the Newcastle-Ottawa Scale, AHRQ = the Agency for Healthcare Research and Quality, ITAS = Insulin Treatment Attitude Scale, MOI = My Opinion on Insulin scale, BIT = Barriers to Insulin Treatment scale

Researchers used different scales to assess psychological insulin resistance, with 8 studies(36.4%) employing the Insulin Treatment Appraisal Scale (ITAS), 2 studies(9.1%) using the Insulin Treatment Attitude Scale, 2 studies(9.1%) employing The Barriers to Insulin Treatment Questionnaire (BIT), 4 studies(18.2%) using the My Opinion on Insulin (MOI) scale, 1 study(4.5%) using the Chinese Attitudes to Starting Insulin Questionnaire (Ch-ASIQ), 3 studies(13.6%) using Self-made scales, 1 study(4.5%) using the Psychological Insulin Resistance Scale and 1 study (4.5%) failing to report the specific measurement tools used (Table 1).

In the 22 studies, 11 studies (50%) employed logistic regression analysis, calculating their odds ratios, and we extracted this data (Table 2), other studies employed chi-square tests, one-way ANOVA or multiple linear regression, without providing odds ratios (ORs).

Table 2.

The results summary of PIR influencing factors

	Year	Author	influencing factors	OR	95%UL	95%LL	P
1	2010	Xiaoying Ding et al. [16]	Diabetes education	0.49	0.3	0.78	0.0029
			Dietary and exercise control	0.6	0.39	0.91	0.0169
			Whether insulin is used	0.26	0.17	0.39	<0.001
2	2011	Yunxia Gao et al. [17]	Knowledge about insulin	1.814	1.298	2.552	0.001
3	2016	Ling Zhou et al. [18]	Gender	-	-	-	<0.001
			Age	-	-	-	<0.001
			Education level	-	-	-	0.04
			Whether insulin is used	-	-	-	<0.001
			Duration of the disease	-	-	-	0.03
4	2024	Shilong Zhang et al. [23]	Gender	1.77	1.063	2.95	0.028
			Average monthly income	0.444	0.216	0.915	0.028
			Duration of the disease	0.387	0.238	0.63	<0.001
			Self-assessed health status	1.706	1.092	2.664	0.019
			Social influence and stigma	1.924	1.166	3.175	0.01
5	2019	Shomba L Rita et al. [24]	Age ≤ 50	2.05	1.98	4.27	0.045
			Diabetic complications	3.33	1.68	6.6	0.001
			Diabetes education	1.96	1.08	3.71	0.04
			Financial burden	2.32	1.08	4.95	0.03
6	2020	Patrick Ngassa Piotie et al. [25]	Negative attitudes towards insulin	1.32	1.12	1.55	0.001
			Reluctance to start using insulin	1.41	1.27	1.57	<0.001
7	2016	Mohammed Ali Batais et al. [26]	Education level	0.52	0.3	0.91	0.023
8	2017	Asif H Gulam et al. [29]	Type of diabetes medication used	5.7	1.3	25.3	0.013
9	2025	Muhammad, H J [32]	Gender	0.226	0.06	0.847	0.072
			Smoke	0.098	0.013	7.1	0.022
			Family history of diabetes	0.039	0.012	0.125	<0.001
			Regular self-monitoring	3.679	1.069	12.661	0.039
10	2025	Bitsang, E [33]	Gender	0.44	0.211	0.921	0.029
			Treatment type	1.58	1.067	2.341	0.023
11	2024	Xiaoxue Han [34]	Fear of addiction	5.677	3.753	7.748	<0.001
			Financial burden	2.436	0.486	4.617	0.016
			Education level	−3.216	−6.755	−1.62	0.002
			Worried about mastering the injection technique	3.294	1.57	6.255	0.001
			The frequency of hypoglycemia is ≥ 2 times per month	2.706	1.028	6.55	0.007
			Whether to take oral diabetes medication	2.075	0.005	6.812	0.04

Abbreviations: PIR = Psychological insulin resistance, OR = Odds ratio, UL = Upper limit, LL = Lower limit

The meta-analysis revealed that female was significantly associated with a higher risk of PIR (OR = 0.38, 95% CI: 0.20–0.72, P = 0.003) (Fig. 1). Conversely, the absence of diabetes education substantially increased the risk of PIR (OR = 1.85, 95% CI: 1.37–2.49, P < 0.0001) (Fig. 2). Furthermore, the presence of negative attitudes towards insulin was a strong predictor of PIR (OR = 1.36, 95% CI: 1.17–1.57, P < 0.0001) (Fig. 3).

Fig. 2.

Forest plot of the impact of diabetes education on psychological insulin resistance in diabetic patients

Fig. 3.

Forest plot of the impact of negative attitudes towards insulin on psychological insulin resistance in diabetic patients

For other factors, due to the fact that only one study provided the OR or no studies provided ORs at all, data integration was not feasible. Therefore, this study did not conduct a meta-analysis on these factors. Instead, it only adopted a systematic review method to conduct a comprehensive analysis of the influencing factors of PIR. We summarized and extracted these influencing factors into five themes, including: patient characteristics, treatment history for the disease, patient attitudes, lifestyle and economic factors, and social-psychological factors.

1. Patient characteristics: including gender, age, education level, average monthly income, diabetic complications, and self-assessed health status. These factors reflect the patient’s basic attributes and level of health cognition that affect psychological insulin resistance.

2. Treatment history for the disease: whether the patient has received diabetes education, whether insulin is used, the type of diabetes medication used, and the duration of the disease, which reflect the different situations of the patient during diabetes treatment and the association with psychological insulin resistance.

3. Patient attitudes: the extent of knowledge about insulin, negative attitudes towards insulin, and reluctance to start using insulin, indicating the role of the patient’s subjective attitude towards insulin in the formation of psychological insulin resistance.

4. Lifestyle and economic factors: dietary and exercise control, financial burden, which involve the impacts on psychological insulin resistance in terms of daily life management and economic stress.

5. Social-psychological factors: social influence and stigma, highlighting the role of the external social environment and psychological perceptions in the patient’s psychological insulin resistance.

Discussion

The findings of this systematic review and meta-analysis provide a comprehensive overview of the factors influencing PIR among patients with type 2 diabetes. Through meta-analysis, gender, history of diabetes education, and negative attitudes towards insulin treatment significantly affected the occurrence of PIR. The identified factors are categorized into five themes: patient characteristics, disease treatment history, patient attitudes, lifestyle and economic factors, and social-psychological factors. These categories highlight the multifaceted nature of PIR, suggesting that it is not solely a product of individual attitudes but is also influenced by broader social, economic, and psychological contexts. All included studies exclusively involved adults (≥18 years old), with mean ages ranging from 47.7 to 65.7 years.

Discussion on the results of Meta-Analysis of the effects of various influencing factors on PIR

From the perspective of gender, female patients have a significantly higher risk of PIR than male patients. This may be related to the differences in the perception and experience of diabetes-related distress between men and women—women are more likely to experience emotional burden, stress, and worry related to blood glucose control or diabetes complications [38].

In terms of negative attitudes towards insulin therapy, patients with negative attitudes have a significantly higher risk of PIR than those without. This also emphasizes the key role of patients’ psychological cognition and emotional tendencies towards insulin therapy in the development of PIR. Specifically, such negative attitudes are manifested in anxiety about injections, the perception that insulin therapy restricts lifestyle, and the view that insulin therapy represents personal failure [25]. The emergence of these negative attitudes is closely associated with a lack of knowledge about insulin therapy [24].

Regarding the history of diabetes-related education, patients who have not received diabetes education have a significantly higher risk of PIR than those who have. This clearly indicates that diabetes education plays an indispensable role in reducing PIR. A lack of diabetes knowledge negatively affects patients’ beliefs and cognition regarding insulin therapy [24]. Only when patients have a more scientific and comprehensive understanding of the disease and insulin therapy can they correctly recognize the necessity and safety of insulin therapy, thereby reducing psychological resistance caused by misunderstandings.

Therefore, more attention should be paid to the psychological issues of female diabetic patients. It is necessary to carry out relevant educational popularization for diabetic patients, enabling them to correctly understand the therapeutic effect of insulin, reduce negative attitudes, and thus lower the prevalence of PIR.

Analysis of the five themes influencing the prevalence of PIR

Patient characteristics

Several studies identified demographic factors such as gender, age, education level, and income as significant predictors of PIR. For instance, some studies found that female patients were more likely to experience PIR, possibly due to gender-specific concerns about body image and the perceived stigma associated with insulin use [39]. Age was another significant factor, with younger patients often exhibiting higher levels of PIR (OR = 2.05, 95% CI: 1.98–4.27), possibly due to a greater fear of the implications of insulin therapy on their lifestyle [24]. Education level and income also played roles, with lower education and income levels being associated with higher PIR, likely due to limited access to information and resources.

Disease treatment history

The treatment history of patients, including their experience with diabetes education, insulin use, and the type of medication used, significantly influenced PIR. Patients who had not received adequate diabetes education were more likely to exhibit PIR (OR = 1.96, 95% CI: 1.08–3.71), highlighting the importance of comprehensive education in improving insulin acceptance [24]. Weitgasser et al. [40] demonstrated that diabetes education and self-management are pivotal components in diabetes care. However, a study [41] indicates that only 48% of patients with type 2 diabetes have undergone diabetes education. For over 70% of participants, their physicians serve as the primary source of such information. As network technology continues to advance, it is imperative to broaden the information sources for diabetes education. This expansion should prioritize the development of more evidence-based online resources, particularly in developing countries that face constraints on medical resources [42, 43]. By doing so, an increased number of patients unable to access adequate medical care can access diabetes education. Additionally, the duration of diabetes and the type of medication used (e.g., oral vs. insulin) were associated with PIR, suggesting that patients may develop resistance as the disease progresses and more invasive treatments are required.

Patient attitudes

Patient attitudes towards insulin, including their knowledge about insulin and their negative perceptions of insulin therapy, were strong predictors of PIR. Patients with limited knowledge about insulin were more likely to resist its use, emphasizing the need for targeted educational interventions. Negative attitudes, such as fears of injections, weight gain, and hypoglycemia, were also common barriers to insulin acceptance [44]. A previous mixed-methods systematic review identified five themes explaining the psychological resistance to insulin therapy. Among these, three were classified as cognitive appraisals, while two were categorized as emotional responses. These emotional responses included the perception of insulin as a source of fear and anxiety, and the viewing of insulin treatment initiation as having profoundly negative connotations, associated with dysregulated emotional states. These findings are consistent with our synthesized results [44]. A previous mixed-methods systematic review revealed that the psychological resistance to insulin therapy is underpinned by five overarching themes. Among these, three are classified as cognitive appraisals, while two are categorized as emotional responses. These emotional responses include the perception of insulin as a source of fear or anxiety and the view that the initiation of insulin therapy is associated with negative implications and dysfunctional emotional states [45]. These findings underscore the importance of addressing patients’ misconceptions and fears through counseling and support.

Lifestyle and economic factors

Lifestyle factors, such as dietary control and exercise, and economic factors, such as financial burden, were identified as significant influences on PIR. Patients who reported poor dietary control and insufficient exercise were more likely to resist insulin therapy, possibly due to a perceived lack of control over their diabetes management [16]. Economic factors, including the cost of insulin and associated supplies, also played a role, with patients from lower socioeconomic backgrounds reporting higher levels of PIR. Socioeconomic data were drawn from adult samples, highlighting affordability as a critical barrier in low-resource settings. This highlights the need for interventions that address both lifestyle management and economic barriers to insulin use. Weiskorn, J et al. [46], propose that future research should focus on the development of more affordable biosimilar insulin products, thereby offering individuals enhanced safety, high-quality, and potentially cost-effective treatment alternatives for diabetes management.

Social-psychological factors

Social and psychological factors, including social influence and stigma, were significant contributors to PIR. Patients who reported experiencing stigma or negative social influence were more likely to resist insulin therapy [47]. A previous study [48] demonstrated that stigma and discrimination constitutes one of the primary barriers to initiating insulin therapy. This suggests that the social environment, including family, friends, and healthcare providers, plays a crucial role in shaping patients’ attitudes towards insulin. Addressing stigma and providing social support may be essential in reducing PIR. All studies focused on adults, emphasizing the need for culturally tailored support in this population.

Implications for clinical practice and future research

The findings of this review have several implications for clinical practice. Firstly, healthcare providers should consider the multifaceted nature of PIR when designing interventions. Tailored educational programs that address patients’ specific concerns and misconceptions about insulin may be more effective than generic approaches. Additionally, addressing socioeconomic barriers, such as the cost of insulin, could improve patients’ willingness to initiate and adhere to insulin therapy.

Future research should focus on longitudinal studies to better understand the temporal relationships between these factors and PIR. Moreover, there is a need for culturally adapted interventions, given the diverse cultural contexts in which PIR occurs. Cross-cultural comparisons could provide valuable insights into the universal and context-specific factors influencing PIR.

The diversity of psychological insulin resistance (PIR) assessment tools

The diversity of PIR assessment tools observed in the included studies has both implications and limitations. Different studies employed a variety of scales such as the Insulin Treatment Appraisal Scale (ITAS) [49, 50], the Insulin Treatment Attitude Scale [16], The Barriers to Insulin Treatment Questionnaire (BIT) [51], the My Opinion on Insulin (MOI) scale [23], the Chinese Attitudes to Starting Insulin Questionnaire (Ch-ASIQ) [52], and even self-made scales.

This diversity reflects the complexity of the construct of psychological insulin resistance. Each tool likely captures different aspects of patients’ psychological responses to insulin. For example, the ITAS might focus on patients’ overall appraisal of insulin treatment, while the BIT could be more attuned to the specific barriers they perceive. This allows for a more comprehensive exploration of the phenomenon from multiple angles. However, it also makes direct comparison between studies challenging. Since the tools have different item compositions and scoring mechanisms, the results may not be directly comparable, potentially leading to inconsistent findings across studies.

Future research could benefit from standardizing the assessment of PIR. This could involve further validation of existing tools or the development of a unified scale that combines the strengths of the various instruments. By doing so, it would enhance the comparability of results across different studies and facilitate a more accurate understanding of the prevalence and determinants of PIR. Additionally, research efforts should be directed towards understanding how different assessment tools relate to specific patient subgroups or clinical settings, enabling more targeted and effective use of these tools in clinical practice.

Limitations

While this review provides a comprehensive analysis of the factors influencing PIR, several limitations should be acknowledged. The heterogeneity of the studies in terms of study design, sample size, and measurement tools limits the generalizability of the findings. Additionally, most of the studies were cross-sectional, precluding causal inferences. Future research should include longitudinal designs to explore the causal relationships between the identified factors and PIR.

In addition, Chinese articles included in this study account for 50%. This phenomenon is related to the rapid increase in the prevalence of diabetes in China since the 21 st century, and its control rate was only 50.1% in 2018–2019 [53], which has promoted the increase in the number of relevant studies. Considering that psychological insulin resistance may be affected by differences in culture, lifestyle and social economy, this may limit the general applicability of the research results.

In conclusion, this systematic review and meta-analysis highlights the complex interplay of factors influencing PIR among patients with type 2 diabetes. Addressing these factors requires a holistic approach that considers not only the individual patient but also the broader social, economic, and psychological contexts in which they Live. By understanding these factors, healthcare providers can develop more effective interventions to improve insulin acceptance and glycemic control, ultimately enhancing the quality of life for patients with type 2 diabetes.

Conclusions

To our knowledge, this represents the first comprehensive evaluation aimed at summarizing the influencing factors of PIR. We have identified a range of critical factors that influence psychological insulin resistance in patients with type 2 diabetes, including patient characteristics, disease treatment history, patient attitudes, lifestyle and economic factors, and social-psychological factors. Insight into these factors is crucial for healthcare providers in designing personalized and effective interventions. Targeting these determinants can enhance patient acceptance and adherence to insulin therapy, thereby improving glycemic control and mitigating the risk of diabetic complications. Nevertheless, additional research is required to investigate the intricate interplay between these factors and to establish comprehensive strategies for the Management of psychological insulin resistance in type 2 diabetes.

Supplementary Information

Below is the link to the electronic supplementary material.

Author contributions

Conceptualization, HZ and ZY; Data curation, HZ, ZY and KL; Formal analysis, DY, MQ, XJ and ZY; Visualization, HZ and KL; Writing—original draft, HZ; Writing—review and editing, HZ and ZY; Supervision, XJ and YF. All authors reviewed the manuscript.

Funding

The Traditional Chinese Medicine Innovation Team and Talent Support Program—National Traditional Chinese Medicine Multidisciplinary Cross-Innovation Team Project—provided financial assistance for this study.

Data availability

All relevant data are within the manuscript and its Supporting Information files.

Declarations

Human ethics and consent to participate

Not applicable.

Ethics approval

This study is a systematic review and meta-analysis of published literature and does not involve original research on human or animal subjects. Therefore, no ethical approval or informed consent.

Consent to participate

Not applicable.

Conflict of interest

The authors declare that they have no competing interests.

Clinical trial number

Not applicable.