Recursive Interplay of Family and Biological Dynamics: Adults with Type 1 Diabetes Mellitus Under the Spotlight

Abstract

Objectives: Diabetes Mellitus involves demanding challenges that interfere with family functioning and routines. In turn, family and social context impacts individual glycemic control. This study aims to identify this recursive interplay, the mutual influences of family systems and diabetes management. Design: Data was collected through a cross-sectional design comparing patients, aged 22–55, with and without metabolic control. Methods: Participants filled out a set of self-report measures of sociodemographic, clinical and family systems assessment. Patients (91) were also invited to describe their perception about disease management interference regarding family functioning. We first examined the extent to which family variables grouped dataset to determine if there were similarities and dissimilarities that fit with our initial diabetic groups’ classification. Results: Cluster analysis results identify a two-cluster solution validating initial classification of two groups of patients: 49 with metabolic control (MC) and 42 without metabolic control (NoMC). Independent sample tests suggested statistically significant differences between groups in family subscales- family difficulties and family communication (p < 0.05). Binary logistic regression shed light on predictors of explained variance to no metabolic control, in four models: Sociodemographic, Clinical data, SCORE-15/Congruence Scale and Eating Behavior. Furthermore, groups differ on family support, level and sources of family conflict caused by diabetes management issues. Considering only patients who co-habit with a partner for more than one year (N = 44), NoMC patients score lower on marital functioning in all categories (p < 0.05). Discussion: Family-Chronic illness interaction plays a significant role in a patient’s adherence to treatment. This study highlights the Standards of Medical Care for Diabetes, considering caregivers and family members on diabetes care.

1. Introduction

Type 1 diabetes mellitus (T1DM) is a lifelong chronic disease that requires repeated daily behaviors that impacts patients’ daily lives and family interactions and routines, namely: meals, glycemic monitoring and symptomatic expression on biochemical changes, such as a hypoglycemia crisis [1,2,3]. Family functioning has a critical role in chronic illness care. The Family Systems-Illness Model (FSIM) [4,5,6] provides a theoretical framework to address illness, the individual and family developments [7,8,9]. It claims that an individual’s adjustment to illness depends on the good fit between the demands of the illness over time and family functioning, considering its life cycle and an individual member’s development. Highlighting interaction and context, the FSIM depicts the family as an interactive system within itself and integrating other systems. This comprehensive model emphasizes the relevance of narratives about disease experience provided by families and their members on dealing with individual maladaptive behavior [10,11]. Once “humans (…) can be defined as language-generating, meaning-generating systems engaged in an activity that is intersubjective and recursive” [12] understanding, family dynamics is deeply attached to Human Communication and Cybernetics Theories. Identifying adequate measures and methodologies that mirror this circular causality in family assessment is crucial. Current clinical research tends to focus primarily on physiological indicators, while psychological and family-level dimensions are often overlooked.

Diabetes management impact on different systemic levels, such as psychosocial well-being [13,14,15,16,17,18,19,20,21,22], marital interaction [23,24,25,26], family functioning and work and other social support [27]. The little existing literature on adults with T1DM and their families [28] focused on how individual characteristics and family functioning can predict effective diabetes management. For example, individual traits/perceptions [29,30,31], coping styles [32], marital support [33,34,35,36], social support [37,38,39,40], diabetes knowledge [14], health-related social control strategies such as overprotection [41]. Methodologically, these studies have been conducted via observational rating scales, clinical semi-structured interviews or self-report instruments. Several authors [42,43,44,45] suggest a combination of quantitative and qualitative analysis to “assist the research in understanding the relations between different family dimensions”. In the last two decades, a 15-item family adjustment instrument—Systemic Clinical Outcome and Routine Evaluation [SCORE-15] has been widely used to monitor outcomes in systemic family therapy [46]. Compared with other similar instruments, it is quicker to apply and score, it is sensitive to change and advisable to routine clinical practice. It has a three-factor structure with factors assessing family strengths, difficulties, and communication. Another instrument, the Congruence Scale that collects holistic information about the internal and external harmony feelings through which the individual reacts in relation to others and the context.

Studying the recursive interplay between disease management and family dynamics in adults with T1DM [47,48] remains insufficiently explored and lacks empirical integration for several reasons. First, studies with couples, families and multi group interventions have been mostly carried out only with T2DM [49,50]. Second, broader research that includes T1DM [51] focuses on other stages of development as childhood or young adult and examines parent-child interactions. Third, T1DM onset can start in childhood or adolescence, revealing a shared history of interrelated meanings about diabetes management [52,53,54,55,56,57,58]. Fourth, the onset of T1DM may also appear later, in adulthood, after one’s relationship or employment status has become consolidated, which involves family actors in a different way [27]. Fifth, family studies with adults with T1DM embody this pathology in global studies about multiple chronic illnesses [10,11]. Finally, there is limited application of systemic models such as FSIM in the T1DM context, especially in studies that correlate family functioning with metabolic control indicators like HbA1c.

This study aims to clarify how family and diabetes management exert mutual influence on each other (Figure 1). It investigates the associations among family functioning, quality of life, psychological congruence, and glycemic control in adults with T1DM. We hypothesized that poor metabolic control is explained by negative effects of diabetes management on the family dynamic, which recursively presents challenges to effective diabetes management. Specifically, the hypotheses that patients with poor metabolic control (NoMC) are expected to report worse family functioning, quality of life, congruence, and emotional eating that significantly predict glycemic outcomes.

2. Materials and Methods

2.1. Overview

After receiving an explanation of the nature and duration of the study, all subjects signed voluntarily an informed consent document as approved by the Ethics Commission of the Faculty of Medicine of the University of Coimbra (protocol code CE-022/2014) in accordance with the Declaration of Helsinki.

2.2. Participants

The study involved 91 adults with T1DM, aged 22–55 (mean age: 36.74 ± 9.08). They were divided into two groups according to Glycated Hemoglobin (Hba1c) values over time: 49 (MC group) mean age: 37.20 ± 9.47, [21,56] and 42 (NoMC group) mean age: 36.19 ± 8.67 [20,56]. Fifty-three volunteers without diabetes (27 males and 26 females, mean age: 35.66 ± 8.51) were also recruited, but given that metabolic status in healthy population is stable and not disrupted (unlike the clinical control group), the value of these data are normative and presented as supplemental material.

The same procedures were applied to all eligible participants: (i) referral to clinical assessment for at least one year at the Department of Endocrinology, Diabetes and Metabolism (EDM, Public Hospital), grouped by HbA1c values over time—note

Table 1. Sociodemographic Characteristics, Clinical and Cognitive data for MC and NoMC groups in mean and standard deviation (N = 91)—People with Diabetes Mellitus—PWDM.

Parameters		Group With Metabolic Control N = 49	Group No Metabolic Control N = 42	X2	t	U	gl	p	d
Sociodemographic data
Gender	Male	31	25	0.13				0.82	0.07
	Female	18	17
Age (in years)		37.2 ± 9.4	36.2 ± 8.6		0.53		89	0.59	−0.11
Marital Status	Single	22	24	1.37			1	0.24	0.07
	Couple	27	18
Household	Alone	17	16	1.69			1	0.43	0.08
	Couple	28	21
	With children	3	5
Income	Stable	33	16	8.94			1	0.003	0.66
	Instable	151	26
Residence (Hospital distance)	Near	20	16	2.97			2	0.23	0.36
	≤1 h	2	17
	>1 h	16	9
Area of Residence	Urban	38	28	1.98			2	0.37	0.06
	Semi-urban	8	8
	Rural	3	6
Educational level	≤12 years	17	27	7.93			1	0.005	0.61
	>12 years	32	15
DM data
Disease onset	<18 ys	24	24	0.61			1	0.38	0.16
	≥18 ys	25	18
DM Duration		17.5 ± 10.3	17.2 ± 9.5		−0.16		89	0.87	−0.03
HbA1c (%) *		7.2 ± 0.6	8.5 ± 1.2		6.32		89	<0.001	0.07
BMI		24.9 ± 3.3	25.2 ± 3.8			989		0.75	0.06
Complications	Yes	21	30	7.94			1	0.006	0.62
	No	28	12
Smoking status	Yes	11	7	0.48			1	0.49	0.14
	No	38	35
Cognitive data
Vocabulary		32.33 ± 3.4	33.60 ± 2.8			807		0.075	0.03
Digit Memory		14.7 ± 2.1	14.10 ± 1.9			1273		0.05	0.41
RPMT		8.04 ± 0.9	8.05 ±1.0			981		0.69	0.08

HbA1c Glycosylated Hemoglobin; BMI body mass index; RPMT Raven’s Progressive Matrices Tests. * Note: Successful or no metabolic control was inferred with at least three and up to five samples of HbA1c since participants began hospital treatment in adult consultation. So multiple time points of Values of HbA1c were collected. This way, a dynamic profile is obtained. Metabolic control (MC) - We included patients with (1) continuously descending and improving values of HbA1c over time (2) patients with low (normal-less than 8%) stable/invariant values that did not change beyond 0.5% and (3) patients whose values varied more than 0.5%, but the maximum value of this oscillation was lower than 8.0%. No metabolic control (NoMC) - We included patients with (1) continuously ascending values of HbA1c over time, (2) patients with high (abnormal- equal or more than 8%) stable values that did not change beyond 0.5% over the time and (3) patients whose values varied more than 0.5%, with the minimum value of this oscillation being more than 8.0%.

. (ii) no other current major chronic disease in the nuclear family, including Diabetes (iii) no cognitive impairments. Participants were excluded if they reported past or current history of neurological and psychiatric disorders, recent diseases, major medical illness (cancer, anemia and thyroid dysfunction) or severe visual or hearing loss. In total, two patients were excluded by presenting a history of psychiatric disorder.

2.3. Sociodemographic, Cognitive and Clinical Features Characteristics

Participants filled out a demographic survey and a cognitive protocol in the presence of the interviewer (Table 1) [59,60]. Participants with more than 50 filled out MoCA (Mont Real Cognitive Assessment) [61]. Body Mass Index (BMI), values of Hba1c and current symptoms or complications were evaluated by clinicians directly or indirectly consulting the patient’s process. Patients fit in with the Metabolic Control Group if they present Continuous Descendent values of HbA1c, low invariable values that did not change beyond 0.5% or values that varied more than 0.5%, but the maximum value of this Oscillation was lower than 8.0%. The inverse pattern characterized the No Metabolic Control Group (NoMC).

2.4. From Family to Diabetes Management

Implications of Family in Diabetes Management were evaluated by applying four questionnaires with adequate psychometric (validity and reliability) properties for the Portuguese population. They covered three levels of systemic evaluation. If any participants had been living in a situation of a couple cohabitation for more than one year, they also completed the marital functioning subscale [62], a 44-item self-report subdivided into two subscales, Marital Functioning and Love. For our research purposes, the Love subscale was not administered.

2.4.1. Individual Level as a Whole

The Congruence Scale (CS) [63] -Portuguese version [64]—evaluates individual functioning and its adaptability in holistic dimensions such as individual connection with the universe/transcendence (the Universal), between people (Interpersonal) and within oneself (Intrapsychic) [65,66]. It is organized into two subscales (Universal and Interpersonal/Intrapsychic) for a total of 16 items answered on a 7-point Likert scale, ranging from 1 (Strong Disagreement) to 7 (Total Disagreement).

2.4.2. Intrafamily Level

Family Functioning was assessed by the Systemic Clinical Outcome and Routine Evaluation (SCORE-15) [67]; Portuguese version [68], a self-report measure (for family members up to 12 years of age) developed to assess outcomes of family functioning in clinical settings. SCORE-15 items are given on a 6-point Likert scale ranging from 1 = “describes us: extremely well”, to 6 = “describe us: not at all” in three subscales: family strengths, family difficulties and family communication. High scores indicate more family problems.

2.4.3. Extrafamily Level

The Inventory of Family Quality of Life (QOL), -Portuguese version- [69], a 40-item instrument, marked 1 (Not Satisfied) to 5 (Completely Satisfied) on a 5-point Likert-scale, covering 11 general areas of individual life satisfaction.

2.5. From Diabetes Demands to Family Conflict

To assess how the challenges of diabetes affect their family, patients completed a survey, based on two instruments: the Diabetes Family Support and Conflict Scale [70] and The Diabetes Family Behavior Checklist [71]. Briefly, it comprises three parts:

• The question, “How does diabetes management contribute to family conflict?”
• A list of Sources of conflict/support between the patient and the family due to diabetes, such as physical exercise, food restrictions, mealtimes, glycemic results and medical advice.
• Patients’ perception about their disease self-management (physical exercise, food, glycemic control, smoking habits), critical problems (food choice, future complications, lack of social support, hypoglycemic episodes, constant efforts to deal with disease) and Eating Behavior, assessed through Portuguese validation of Dutch Eating Behavior Questionnaire, DEBQ [72,73]. It is a 33-item instrument which evaluates three types of eating styles such as restrained (avoid eating more than initially defined), external (eating motivated by external factors such as the food’s good smell and appearance) and emotional (eating in response to emotions).

2.6. Data Analysis

We used IBM SPSS Statistics (version 24) to conduct data analysis. Descriptive statistics are reported as mean ± SEM. Prior to analysis, raw data were examined for normality by the Shapiro-Wilk goodness-of-fit test [74]. Firstly, we examined the extent to which family variables grouped dataset to determine if there were similarities and dissimilarities that fit with our initial diabetic groups’ classification. Instead of one multivariate method, we calculated K-means and two-steps algorithms so that consistent results could be achieved, as proposed by Kos and Psenicka [75]. If a two-group classification is found and replicated with a different method, a two-clusters solution related to diabetic groups bipartition is reinforced. No hierarchical cluster analysis was used given that we would like to test the two-cluster hypothesis. We introduced only continuous variables because K-means cluster analysis does not support categorical ones. Previously the cluster analysis, variables were standardized to minimize dimensional statistical errors [76]. Both K-means and Two steps methods used centroid distance with Squared Euclidean distance as the similarity measure. For K-means measure we calculate Chi-squared statistics to determine the percentage of correspondence between clusters found and dynamic HbA1c categories. Continuous variables were analyzed using a series of independent-samples t-tests, if normality and variances homogeneity was assumed. To examine the main predictors of barriers to diabetes management, we carried out a binary logistic regression, choosing dynamic variable of Hba1c as the dichotomous dependent variable (MC and NoMC). We examined intercorrelations (Pearson’s) to accomplish the assumption of no multicollinearity to regression analysis, with 0.8 meaning a huge correlation [77]. We conducted four regressions, each one related to a group of distinct variables such as sociodemographic data (1), clinical features (2), family (3) and eating behavior (4), resulting in four final models. Statistics are reported with 95% confidence intervals [95% CIs]). Null-hypothesis statistical tests were evaluated according to an alpha value of 0.05. The chi-squared test was used to compare categorical variables, and nonparametric tests (Kruskal-Wallis) were used to compare ordinal variables.

3. Results

3.1. Two Cluster Solution and Metabolic Control Bipartition

The two-cluster solution was verified at both cluster analysis methods, matching each cluster with diabetic group’s bipartition in similar proportions (MC and NoMC). We introduced only continuous variables: general results of 1) family functioning, 2) quality of life and 3) congruence, since data reduction could be achieved. These three general results are significantly (p < 0.01) and moderately correlated: SCORE-15 with QoL (r = −0.57) and CE (r = −0.471) and QoL with CE (r = 0.34). K-means cluster analysis indicates that all variables have significant weight to the formation of a two solution clusters agglomeration, p < 0.001, by ANOVA output: SCORE-15 [F (89) =102.54]; CE [F (89) =52.83] and QoL General [F (89) =73.19]. X²(1) =26.05, p < 0.001 informs that 76.9% (40/52) of MC group belongs to cluster 1 and 76.9% (30/39) of NoMC group belongs to cluster 2, supporting our group classification. Two-steps cluster analysis showed a high silhouette coefficient (=0.5) and a size ratio of 1.39 (53/38), near the size ratio of the dataset (1.22; 49/42). Posterior inclusion of dynamic variable of HbA1c related cluster 1 with the MC group and cluster2 with the other group. The positive or negative direction of each variable was obtained in cluster comparison, which confirms the correlations results. The MC group is characterized by lower results on SCORE-15 (indicating high family functioning) and higher on General Quality of Life and General Congruence. For the NoMC group, the other cluster, we observed the opposite direction (Figure 2).

3.2. Family System. Implications Diabetes Management

Forward analysis with independent sample parametric and non-parametric tests allowed us to deepen dive on group differences related to family system instruments. The group with NoMC scored higher on Family Difficulties and Family Communication (SCORE-15), presented low Quality of Life (QoL) and less connection with themselves, others and the context (Congruence Scale).

Table 2. Family System Comparison. Descriptive statistics and results of mean comparison between groups for subscales of SCORE-15, Congruence Scale and Quality of Life and (N = 91).

Parameters	Group With MC (n = 49)					Group No MC (n = 42)
	M	SD	1st Q	2nd Q	3rd Q	M	SD	1st Q	2nd Q	3rd Q	U	t	gl	p	d
Score-15
Family Strengths	1.6	0.6	1.4	1.8	2.1	1.8	0.6	1.2	1.4	2.0	1141.5			0.002	0.33
Family Difficulties	1.6	0.6	1.8	2.6	2.8	2.4	0.7	1.2	1.6	2.0	411			<0.001	1.22
Family Communication	1.8	0.6	2.2	2.8	3.2	2.68	0.7	1.4	1.8	2.1	42.3			<0.001	1.16
Congruence
Intra/Interpersonal	48.5	7.3	45.5	50.0	53.5	42.8	8.3	37.7	42.0	48.0	1495.5			<0.001	−0.73
Universal Congruence	33.7	10.7	28.5	35.0	42.0	25.4	9.2	17.0	25.0	32.0	1504			<0.001	−0.83
Quality of Life
Financial	22.3	4.8	19.0	22.0	27.0	19.1	4.3	16.0	19.0	22.0	1430.5			<0.001	−0.72
Time	12.4	2.9	11.0	13.0	15.0	11.5	2.1	10.0	11.5	13.0		−1.56	89	0.120	−0.33
Neighborhood	20.3	3.7	18.0	20.0	23.0	18.3	3.3	16.0	18.0	21.0		−2.69	89	0.009	−0.57
Home Conditions	18.2	3.3	16.0	18.0	20.0	18.3	3.0	16.0	18.5	20.2		0.16	89	0.870	0.03
Mass Media	9.2	2.1	8.0	9.0	10.0	9.2	2.5	7.0	9.0	11.0		0.03	89	0.978	0.01
Social/Health Relationships	14.9	2.4	14.0	15.0	16.0	12.8	2.4	11.0	13.0	14.0	1553			<0.001	−0.86
Job	9.9	2.7	8.5	10.0	11.5	8.6	2.2	7.0	8.0	10.0		2.44	89	0.017	−0.51
Religion	6.3	1.9	6.0	6.0	8.0	5.1	2.3	3.0	5.0	7.0	1365			0.006	−0.64
Family/Marital	8.2	1.7	8.0	8.0	10.0	6.9	1.9	6.0	7.0	8.0	1447.5			0.001	−0.72
Children	6.9	2.1	5.0	7.0	9.0	7.0	2.1	5.0	7.5	8.2	1026			0.981	0.03
Education	7.4	1.4	7.0	8.0	8.0	6.4	1.4	5.0	6.0	8.0	1411.5			0.002	−0.73

summarizes the results.

Knowing the group differences, we studied which variables explained the variance of no metabolic control—the predictors of no glycemic control. Results are summarized in

Table 3. Predictors of HbA1c Summary of binary logistic regression analyses of four categories of variables (Sociodemographic, Relevant Clinical Data, Family Systems and Eating Behavior) predicting participants’ NoMC.

Parameters	Binary Logistic Regression No Metabolic Control Category
	B	Exp(B)	95%IC	p
Sociodemographic data
Income	−1.22	0.29	0.12–0.74	0.009
Level of education	−1.29	0.27	0.11–0.68	0.005
Clinical data
HbA1c Values	1.73	5.62	2.59–12.24	<0.001
Family System
SCORE-15	1.43	4.17	1.15–11.27	0.005
Congruence	−0.05	0.96	0.92–0.99	0.01
Eating Behavior
Emotional Ingestion	0.69	2.27	1.24–4.13	0.008

The model containing Income and Educational Level was statistically significant (X² (2) = 16.28, p < 0.001, R² Negelkerke = 0.22). The model related to clinical features was significant (X² (2) = 43.17, p < 0.001, R² Negelkerke = 0.51) being reduced from Distance to Health Services, Length of disease, disease onset, IMC, Smoking Habits to HbA1c values as predictor variable and explained 81.3% of the variance of risk to NoMC. The third model explains 77.4% of the variance of NoMC in PWD (X² (2) =28.87, p < 0.001, r² = 0.393. The fourth model explains 70.3% of the variance to NoMC, remaining Emotional Ingestion (X² (1) = 8.07, p = 0.005, R² Negelkerke = 0.11.

. Income, Educational level (first model), HbA1c values (second model), SCORE-15 & CE (third model) and Emotional Eating Behavior (fourth model) proved to be significant predictors of lower metabolic control.

Participants with diabetes that cohabit for longer than one year also filled out the subscale Marital Functioning of EASAVIC, N = 44 (18, NoMC; 27, MC). The group with MC scored higher than the other group for all variables studied (see

Table 4. Marital functioning (EASAVIC subscale) results for participants with diabetes (N = 45).

Parameters	Group With Metabolic Control (N = 27)					Group No Metabolic Control (N = 18)
	M	SD	1st Q	2nd Q	3rd Q	M	SD	1st Q	2nd Q	3rd Q	U	p	d
Total Marital Functioning	3.8	0.7	3.4	3.8	4.3	2.8	0.6	2.2	2.7	3.4	406	<0.001	1.21
Family Functioning	4.7	1.1	3.8	4.7	6.0	3.6	1.0	2.5	3.3	4.5	376.5	0.002	1.29
Free Time	3.9	1.1	3.0	4.0	5.0	2.9	0.9	2.0	3.0	3.6	365.5	0.004	1.33
Autonomy	3.9	1.0	4.0	4.7	5.5	4.7	0.9	3.0	3.7	4.7	353	0.010	1.36
Extra Fam Relationships	3.7	0.9	4.3	4.7	6.0	4.9	0.8	3.0	3.6	4.1	408	<0.001	1.21
Communication & Conflict	3.5	1.0	4.5	5.0	5.7	4.9	1.0	3.0	3.1	4.5	400	<0.001	1.23

).

3.3. From the Demands of Diabetes to Family Conflict

According to question 1 “How does diabetes management contribute to family conflict”, NoMC showed a moderate level of conflict (47.6%) while MC perceived low level of family conflict (71%). This difference was statistically significant [X² (2) =11.74, p = 0.003], indicating an association between group and perception of family conflict. We found similar results related to family support [X² (2) =9.54, p = 0.002], given that 87.8% of people with MC reported having support as compared with 59.5% of the NoMC group. The first major source of conflict for NoMC was “annoying me to follow the doctor’s advice” (23.8%) while MC group 36.7% pointed out “no sources of conflict” (36.7%). The second source of conflict was “when they tell me what I can’t eat” (18.4% for MC and 21.4% for NoMC). Mealtime is a major concern, so the person preparing the meal plays an important role. In our sample, there was no association between groups and who cook at home [X² (2) = 0.84, p > 0.05]. However, it is related to gender since 91.4% of females cook by themselves whereas males relegate this task to their mothers (44.6%) or their wives (26.8%), thus presenting a statistically significant difference [X² (2) = 34.15, p < 0.001]. Finally, the MC group worried more about future complications (69.4%) than the NoMC (33.3%), which is also focused on daily, present and permanent efforts required by disease.

4. Discussion

Three main conclusions can be drawn. First, this study found a coherent meaning in family to binary characterization of diabetes management based on a related biological variable (dynamic values of HbA1c). Second, findings supporting group differences are consistent with previous studies reinforcing the recursive interplay of family variables and diabetes management. Third, even though this study does not focus on intervention, it points to specific information that may help to design interventions in a “simple, easily operational and clinically relevant” manner [78].

As for the first statement, a two-solutions cluster analysis based on self-report measures encourages family assessment of adults with T1DM in health and clinical settings. SCORE-15 is a promising candidate to take part in an interdisciplinary protocol assessment by the health team. As recommended by American Diabetes Association, “providers should consider an assessment (…) in the initial visit, at periodic intervals, and when there is a change in disease, treatment, or life circumstance. Including caregivers and family members on this assessment is recommended” [2].

Concerning the second conclusion, previous literature [35,46] related poor diabetes management with communication patterns, overwhelming feelings or thoughts, congruence and emotional eating. A study carried out in Italy with T1DM adults also points that family rigidity communication has a mediator effect between depression symptoms and poor glycemic control [79]. Other similar chronic diseases caused by DM as Wolfram syndrome should be considered [80]. A pattern of hopelessness and exhaustion on results from sources of family conflict and low congruence seems to be consistent with emotional eating as a major predictor, instead of restrained or external eating behavior. Meal preparation is one of the most frequent sources of family conflict for both groups. It is a mark of gender regardless of marital status (mothers and sons). This can encourage a deeper review of nutritional interventions based on adherence to regimen changes, family meal routines or food habits related to feelings [81]. Additionally, patients’ reports of others’ support could be a source of conflict, translating into annoying but well-intentioned expressions of concern. Controlling health behaviors such as overprotection may damage a patient’s management in both parental and couple relationships in adulthood [82]. Thus, mutual perceptions of caregivers and patients should be considered. As Martire & Helgeson [58] states “parent or spouse involvement in illness management can be viewed as ranging from under involvement to over-involvement, with the extremes being associated with poorer management.”

Third, our findings inform prevention and intervention programs based on “four interlocking triangles” made up of the four components of the “Therapeutic Quadrangle”: the illness, the family, the patient and the health-care system” framed in the context [5]. Placed within multidisciplinary teams, design of theory-based interventions should outline social, family and marital support and their perceptions; caregivers and their role in diabetes management at home; communication patterns and problem-solving skills for family members and couples; individual and family developmental life cycle considering life transitions and its normative and unpredictable tasks; family history of coping with the illness; shared disease knowledge and illness management skills; beliefs systems related to health care, the health system, health providers and medicines [83]; eating behavior considering emotional assessment and workplace conditions. Interventions should also consider sociodemographic data (financial concerns and education level), once it is related to reports of unstable values of HbA1c (unemployment, particularly) perhaps because it limits individual choice. Besides repercussions on family dynamics [84], it exhibits biological direct interference for patients. Training psychologists to specifically provide psychosocial care for patients with diabetes is inherent to intervention programs of which there are too few [85,86,87]. Intervention with family and individual mental health well-being should be timely fashion to avoid symptomatic evolution to complex levels of interventions with cost effects. Family life cycle, individual development, or disease challenges are all present in several domains in the patient description. So, Rolland’s Family Systems-Illness Model fits for theoretical and practical comprehension of relationship-based approaches to health and illness management and should be adopted for clinical interventions. The Circumplex Model of Family Systems from Olson was already tested as a relevant family functioning instrument, highlighting family cohesion and flexibility as good predictors of glycemic control. But, quickly and easier, the family functioning could be screened using the SCORE-15 by diabetes educators at intake, followed by referral to counseling if needed [7,46,88,89,90].

The present study has some limitations. First, depression, anxiety or other emotional problems were not verified. The prevalence of depression among adults with diabetes is higher than in adults without diabetes [12,84]. Second, once the population of the study has diabetes in a chronic phase [5], conclusions should be not extended to diagnosis or the terminal phase.

Future family research studies could focus on several issues. (1) narratives built around growing up with diabetes offered by patients, their caregivers and people without diabetes (“what is transformed or preserved through time”, Melo & Alarcão [43]); in order to understand the disease’s impact on future choices, such as careers and close relationships; (2) family assessment, as SCORE-15, helping to improve family assessment screening to evaluate therapeutic process evolution; (3) mixed methodologically are recommended, such as self-report measures and interviews with circular questioning techniques, dyadic problem-solving interactions, or observations at different times and integrating different family members.

5. Conclusions

In this study, we divided a sample of 91 adults with type 1 diabetes into two groups concerning metabolic control over time to explore the recursive interplay between biological, family and social dynamics within diabetes management. Despite the notable absence of studies that address adults with T1DM and their families, this is one of the first studies applying systems theory to adult T1DM care, highlighting the need for incorporating family assessment tools, as SCORE-15, into routine clinical care for adults with T1DM. This study revealed transversal results in individual, family and large contextual systems that are interconnected and include all diabetes management stakeholders. However, family-based intervention approaches in a collaborative multidisciplinary team, through the diagnosis phase, face a slow process in the health system in Portugal and other world regions.