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FUNDING INFORMATION | The study was funded by The Research Council of Norway. | PMC10099854 | ||
CONFLICT OF INTEREST | The authors declare no conflicts of interest. | PMC10099854 | ||
INFORMED CONSENT | Participants were informed with a written consent. | PMC10099854 | ||
ACKNOWLEDGEMENT | We thank the subjects for their time, effort, and cooperation during this project. The authors declare no conflicts of interest. The results of this study are presented clearly, honestly, and without fabrication falsification, or inappropriate data manipulation. The study was funded by The Research Council of Norway. | PMC10099854 | ||
DATA AVAILABILITY STATEMENT | The data that support the findings of this study are available from the corresponding author upon reasonable request. | PMC10099854 | ||
REFERENCES | PMC10099854 | |||
Background | mental illness, PLWS, psychiatric, schizophrenia, hopefulness | Hopefulness is a positive orientation or state of mind that can aid in the recovery and treatment of mental illness, as it can have significant impacts on clinical and psychosocial outcomes. As resource-constrained settings work to implement recovery-oriented care, there is a need to better understand hopefulness among people living with schizophrenia (PLWS) and caregivers in their extended family networks. This study seeks to examine the dyadic relationship of hopefulness and its associated correlates among PLWS attending outpatient psychiatric clinics and their caregivers in Tanzania. | PMC10339619 | |
Methods | PLWS, hopefulness | This study utilized baseline and immediate post-intervention data collected as part of a randomized controlled trial testing a culturally tailored model of Family Psychoeducation, KUPAA, in Tanzania. The Herth Hope Index was used to measure hopefulness among PLWS ( | PMC10339619 | |
Results | PLWS, lower disability | Better family functioning was associated with higher levels of hopefulness in PLWS and their caregivers. Lower levels of stigma, lower symptom severity, and lower disability were associated with higher levels of hopefulness in PLWS. For PLWS and their caregivers, actor effects from the APIM model were less than one (PLWS, | PMC10339619 | |
Conclusion | PLWS, hopefulness | Hopefulness is important to consider in family or caregiver-based treatments for PLWS because caregiver hopefulness may influence improvements in hopefulness among PLWS over time. Future studies should further explore the longitudinal dyadic relationship of hopefulness for these populations, as hope is a non-pharmacological and modifiable mechanism of change that is underutilized in care and treatment plans for PLWS globally. | PMC10339619 | |
Trial registration | Clinical Trials #NCT04013932, July 10, 2019. | PMC10339619 | ||
Supplementary Information | The online version contains supplementary material available at 10.1186/s12888-023-04990-8. | PMC10339619 | ||
Keywords | PMC10339619 | |||
Background | schizophrenia [Mental illness, PLWS, psychiatric, Schizophrenia, schizophrenia, hopefulness | DISORDER, SECONDARY | Schizophrenia is a severe psychiatric disorder that affects 21 million people worldwide [In low- and middle-income countries like Tanzania and high-income countries like the United States, there are high rates of relapse and rehospitalizations for schizophrenia, highlighting the urgent need for accessible, recovery-oriented care globally [Existing evidence indicates that families are important for influencing clinical and social outcomes of their relatives living with schizophrenia [Hope may be an important construct in recovery-oriented care of schizophrenia because it facilitates agency, self-efficacy, and pathways to healthier lives [Hope goes beyond optimism and has been defined several ways across different disciplines [Hope can have positive impacts on both patient and caregiver populations. Hopefulness positively influences clinical and psychosocial outcomes, as made evident in several studies with varying chronic indications [There is limited quantitative evidence available on the role of hopefulness in improving the mental health of PLWS as well as their caregivers. Most of the existing literature is set in high-income countries, is qualitative, and/or focuses solely on the individuals living with schizophrenia [Mental illness does not occur in a vacuum and is instead often experienced within the context of one’s family, which may be even more critical to consider in more collectivist cultures [The aims for this secondary analysis paper were 1) To identify the sociodemographic and illness-related correlates that may be associated with hopefulness separately for PLWS and their caregivers, and 2) To explore the dyadic relationship of hopefulness among PLWS and their caregivers in Tanzania. | PMC10339619 |
Methods | PMC10339619 | |||
Study overview | Psychotic Disorders, ’ | All data used in this study were collected as part of the pilot individually randomized group treatment trial titled: “Family Psychoeducation for Adults with Psychotic Disorders in Tanzania” (KUPAA), funded by the National Institute of Mental Health (NIMH) [R34MH106663]. KUPAA is a Swahili word meaning ‘to soar’ and it stands for Kuwezeshana kupata uzima which means ‘supporting one another for wholeness’. See Clinicaltrials.gov #NCT04013932 for trial results. Data for the present study included all eligible study participants in both arms of the KUPAA trial. | PMC10339619 | |
Study setting | MZRH, psychiatric | The KUPAA study was conducted in Dar es Salaam and Mbeya regions, located in the East African country of Tanzania. The first study site was Muhimbili National Hospital (MNH), which is located in the major urban city of Dar es Salaam. MNH is the national referral hospital with a catchment area of about 4 million people. The Department of Psychiatry and Mental Health has both inpatient and outpatient care, with 70 beds in total, usually fully occupied. Psychiatrists, psychiatric nurses, social workers, psychologists, and occupational therapists work together to provide care at this facility.The study also took place at the Mbeya Zonal Referral Hospital (MZRH), located in the southern highlands zone. MZRH is the only referral facility in the southern part of Tanzania with eight districts and it also acts as a referral facility for neighboring regions. The Psychiatry and Mental Health Unit has 24 beds in total, which are also typically fully occupied. One psychiatrist, along with general practitioners, psychiatric nurses, a psychologist, and social workers provide care at MZRH. | PMC10339619 | |
Participants | schizophrenia | DISEASE | A total of 66 dyads composed of individuals living with schizophrenia and their caregivers were included in the study. The study was powered for the primary aim of the clinical trial, which was to assess intervention efficacy, and not for the APIM analysis. All treatment-engaged patient participants had an ICD-10 (International Classification of Disease) diagnosis of either schizophrenia (F20, | PMC10339619 |
Procedures | schizophrenia | SYNDROME, POSITIVE | Baseline data collection took place in September and October of 2019, the intervention was delivered from November 2019 to February 2020, and immediate post-intervention follow-up data collection occurred from March through June of 2020. Written informed consent was obtained from all participants after being screened for study eligibility. Individuals living with schizophrenia were required to be stable at the time of consent, which was determined by the study psychiatrists. To ensure that these individuals were able to give adequate informed consent, the research team revisited the consent form with them prior to the follow-up interview. All participants were compensated 7,500 Tsh (~ $3.50 USD) at each interview for costs related to study attendance.Study visits and data collection occurred in office facilities within MNH and MZRH. Research assistants administered all patient interviews with self-reported assessment measures, including sociodemographic information, except for the clinician-rated measure, the Positive and Negative Syndrome Scale (PANSS) [ | PMC10339619 |
Measures | schizophrenia, disability | REGRESSION, SYNDROME, POSITIVE | This study includes measures for PLWS and their caregivers. The World Health Organization’s four-step translation and cultural validation process, namely, forward-translation, back-translation, pre-testing, and finalization with expert consensus, was implemented for all scales in the study [Hopefulness, our dependent variable in the regression and APIM analyses, was measured using the Herth Hope Index (HHI) [Religiosity was measured for all participants using the Duke University Religion Index (DUREL) [The Internalized Stigma of Mental Illness (ISMI) scale assesses experiences with stigma in participants living with schizophrenia [The severity of symptoms experienced by PLWS was measured using the clinician-rated Positive and Negative Syndrome Scale (PANSS) [Family functioning was reported by all participants using the 15-item version of the Systemic Clinical Outcome and Routine Evaluation (SCORE-15) [The level of disability in PLWS was assessed using the World Health Organization Disability Assessment Schedule-Second Version (WHODAS 2.0) [Burden experienced by caregiver participants was measured utilizing the Burden Assessment Scale (BAS) [ | PMC10339619 |
Results | PMC10339619 | |||
Participant characteristics | Table Table | PMC10339619 | ||
Correlates of hopefulness | REGRESSION | Supplemental Table Results of univariable and multivariable linear regression models estimating mean hopefulness for PLWSTable Results of univariable and multivariable linear regression models estimating mean hopefulness for caregivers | PMC10339619 | |
APIM analysis | The results of the APIM analysis are depicted in Fig. Results of APIM analysis. Time 0 is baseline data collection and time 1 is data collection immediately post-interventionIn an APIM model, the joint effect, or the additive effect of both actor and partner baseline HHI score on follow-up actor HHI score, differs depending on the level and direction of baseline scores. The mean HHI scores at baseline and follow-up of both caregivers and PLWS are displayed in Table Mean total Hopefulness score, by Time, in PLWS & CaregiversTable Model predicted follow-up HHI Total score values for hypothetical values of baseline actor and partner HHI Total scores for a PLWS in the intervention (KUPAA) trial armPartner effects for caregivers and patients. Predicted patient HHI Total score at post-intervention follow-up by changes in caregiver baseline score, holding patient baseline HHI Total score at its observed mean (left panel); and predicted caregiver HHI Total score at post-intervention follow-up by changes in patient baseline score, holding caregiver baseline HHI Total score at its observed mean (right panel) | PMC10339619 | ||
Discussion | mental illness, illness, PLWS [A, cancer, PLWS, disability, hopefulness | CANCER, RECRUITMENT | The current study examined the independent and dyadic associations between hopefulness and selected predictors within the PLWS-caregiver relationship. Hopefulness in our study population exhibits interdependence meaning dyad partners influence one another’s level of hope over time. To the best of our knowledge, there is no current literature on the interdependence of hopefulness among PLWS and their caregivers for comparison. Family functioning was found to be an important correlate of hopefulness among both participant groups. Better family functioning was associated with higher levels of hopefulness, suggesting that the maintenance of a healthy relationship with one’s dyadic partner is essential. This finding speaks to the interdependent nature of hopefulness among this population and suggests that facilitating healthy relationship dynamics could promote positive outcomes in both PLWS and caregivers.Among PLWS, we found various psychosocial and clinical factors to be important correlates of hopefulness. Precisely, lower levels of internalized stigma, symptom severity, and disability were associated with higher levels of hopefulness. This aligns with findings from the limited studies that have explored correlates of hopefulness among PLWS [A positive partner effect was identified among PLWS. This indicates that higher caregiver hopefulness is associated with higher patient hopefulness at follow-up. Therefore, caregivers may play a significant role in influencing hopefulness levels among PLWS. Facilitating hopefulness in caregivers may ultimately be important for improving psychosocial and clinical outcomes in PLWS. A negative partner effect among caregivers was identified, indicating that higher hopefulness among PLWS is associated with lower caregiver hopefulness at follow-up. This finding is only somewhat unexpected. There is literature to indicate that PLWS who have low insight (illness awareness) have more difficulties in the recovery process which could include goal setting alignment with clinicians and caregivers who are more or less hopeful about what is possible for the future given symptomology and functioning [This study has several limitations, including its small sample size of dyads (Second, hopefulness is a latent construct that is subject to measurement error. The HHI instrument has not been validated in Tanzania and may not have accurately captured the experience of hopefulness in our study population despite instrument translation and adaptation. It is worth noting that the Herth Hope scale was found to be highly correlated in our data with a second local measure of hopefulness, the Helen Siril Hope scale, which was originally developed for HIV populations (ρLastly, the study sample may be not representative of the larger population of PLWS and their caregivers in Tanzania. Participants were eligible to participate if they were receiving outpatient services at either of the study sites. This criterion likely excluded several affected individuals due to the logistical and financial challenges associated with accessing care in Tanzania. Additionally, symptoms among PLWS had to be stable at the time of informed consent which likely led to the exclusion of affected individuals who were experiencing an acute episode of their illness at the time of study recruitment. As many of the PLWS in our study are reliant on their relatives for organizing their treatment and appointments, our study may exclude less involved caregivers. Consequently, hopefulness levels may be higher in the present study than in the general population of PLWS in the areas studied.Tanzania’s Disabilities Act of 2010, which legally requires informal family caregivers to take financial and social responsibility for individuals with disabling levels of mental illness, could be improved upon with a government-backed statutory financial safety net. While caregivers are needed for a range of psychosocial supports, removing the additional financial burden could help them better carry out their roles effectively while remaining hopeful. Additionally, caregivers should be included in clinic and community-based interventions targeted at fostering hopefulness alongside patients. Similar to the intervention implemented by Chan et al. in persons recovering from cancer, a clinic-based hope intervention may consist of therapy on topics including goal setting, identification of pathways to achieve goals, and positive self-talk [To the best of our knowledge, this study is the first to examine the longitudinal, dyadic relationship of hopefulness among PLWS and their caregivers in resource limited settings, such as Tanzania. As indicated in other studies conducted in Tanzania, family structures, roles, and responsibilities are both traditional and evolving with rapid social change (e.g. deferential respect for parents/elders who are often caregivers, alongside a potential lessening of extended family support for caregiving, moving towards more nuclear family structures, particularly in urban areas [Qualitative research may be particularly beneficial to conduct on this topic, as hopefulness is a multifaceted construct that is influenced by culture and context. Qualitative research should be conducted among both PLWS and their caregivers in order to further reveal the complexities of hopefulness as experienced in the family context. | PMC10339619 |
Conclusion | schizophrenia, PLWS hopefulness | The results of our study suggest that hopefulness may be important to consider in PLWS treatment regimens as caregiver hopefulness is associated with improvements in PLWS hopefulness over time. Dyads of this nature are complex, and members are continuously influencing each other’s outcomes. Neither schizophrenia nor hopefulness are experienced in a vacuum. Therefore, caregiver mental health and well-being are absolutely critical to consider when working to promote recovery in individuals living with schizophrenia. Hope is a powerful non-pharmacological tool that is underutilized in both high-resource and resource-constrained settings. | PMC10339619 | |
Acknowledgements | We thank our colleagues at the Muhimbili National Hospital and Mbeya Zonal Referral Hospital in Tanzania for supporting this clinical trial. We are also incredibly grateful to the affected individuals that gave their time and energy to our study. | PMC10339619 | ||
Authors’ contributions | AM wrote the first draft of the manuscript. AM, JRE, and JNB contributed to the design, implementation, and analysis of the study and edited the first draft. All authors revised drafts and approved the final manuscript. | PMC10339619 | ||
Funding | Funding was received from the National Institute of Mental Health (NIMH; award 5R34MH106663). | PMC10339619 | ||
Availability of data and materials | SECONDARY | The dataset generated and analyzed during the current study is not publicly available per IRB requirements, but it is available from the senior author (JRE) on reasonable request, with permission from study PIs, and with IRB approval for secondary analyses to maintain confidentiality. | PMC10339619 | |
Declarations | PMC10339619 | |||
Consent for publication | Not applicable. | PMC10339619 | ||
Competing interests | None. | PMC10339619 | ||
References | PMC10339619 | |||
1. Introduction | Ten percent of Americans are food-insecure. Few known studies have accessed college food insecurity via random sampling. An online cross-sectional survey (Food insecurity is a global health concern. A total of 13.5 million Americans (10.2%) have experienced food insecurity at some point within the last year [The USDA does not directly measure food insecurity among college student populations, but rather US adults, households, and children [Qualitative studies have sought to explore the specific factors that might lead to college student food insecurity and found that food-insecure college students are having to sacrifice their food budgets due to having fixed bills [This late adolescence and early adulthood period is characterized by those of college age and possibly living on their own for the first time [Understanding the profile of a food-insecure college student can potentially provide better insight to colleges and universities of how to pinpoint or target possible students that might be food-insecure. The goal of this study was to describe the profile of a food-insecure college student by assessing the prevalence of food insecurity, the history of government assistance, and food shortage contacts by food security status among a random sample of enrolled college students at a major southeastern university. | PMC10005036 | ||
2. Materials and Methods | PMC10005036 | |||
2.1. Sample | A self-administered online survey via Qualtrics was sent to a simple random sample of 5000 currently enrolled University of Florida (UF) undergraduate students. The total undergraduate population at UF was 35,043 (female: 19,355 (55.2%), male: 15,688 (44.8%)). A request was made to the UF Office of Institutional Planning and Research for the simple random sample of currently enrolled undergraduate students. The Excel file received only contained student emails with no other identifying information provided. No incentives were provided to students for participation. Data were collected from 25 April to 3 June 2017. In order to participate in the study, students had to: (1) be currently enrolled (full-time or part-time) at the University of Florida, (2) be undergraduate students, (3) have the ability to read and write in English, and (4) have a valid UF email address. Overall, 1087 students completed the survey, resulting in a response rate of 21.7%.Potential respondents were emailed an introductory letter describing the study, potential benefits and harms of participation, Institutional Review Board (IRB) contact information and approval/number, and an individualized/personalized link to the survey. Additionally, respondents were not offered an incentive to take the survey. Respondents had two weeks to complete the survey after they started. Consistent with the Dillman Total Design Method of survey administration, after the introductory email, three automated personalized reminder emails were sent to non-respondents every three days [ | PMC10005036 | ||
2.2. Measure of Food Insecurity | The six-item food security scale is an instrument created by the USDA developed to determine the level of food security of individuals [There are six-items from the USDA Food Security Short Form which measure food insecurity experienced within the last 12 months. These six items are:“In the last 12 months, the food that (I/we) bought just didn’t last, and (I/we) didn’t have money to get more.”“In the last 12 months, (I/we) couldn’t afford to eat balanced meals.”“In the last 12 months, since last (name of current month), did (you/you or other adults in your household) ever cut the size of your meals or skip meals because there wasn’t enough money for food?”“How often did this (cut the size of or skip meals) happen—almost every month, some months but not every month, or in only 1 or 2 months?”“In the last 12 months, did you ever eat less than you felt you should because there wasn’t enough money for food?”“In the last 12 months, were you ever hungry but didn’t eat because there wasn’t enough money for food?” | PMC10005036 | ||
2.3. Measure of Government Assistance | Respondents were asked “if their family ever obtained government assistance during childhood (government housing, free/reduced lunch in school, SNAP, Women, Infants, and Children (WIC), and food banks)”. Respondents had the option to “choose all that apply” from the listed options. | PMC10005036 | ||
2.4. Measures of Food Shortage Contacts | For the last item, respondents were asked to “choose all that apply” as to whether they felt comfortable discussing with a parent, roommate, counseling and wellness personnel, advisor/mentor, resident assistant, or professor if they were running short on food. | PMC10005036 | ||
2.5. Data Analysis | The data for this study were analyzed using JMP Pro 12 (SAS Institute Inc., Cary, NC, USA, 1989–2021) software. Descriptive statistical analyses were used to provide group percentages and compare differences among the individual variables. Sociodemographic characteristics that were measured include: current college classification (lower class (freshman/sophomore), upper class (junior/senior)), gender identity (male/female), race (white/non-white), current age, parental status, first-generation college student, current grade point average (GPA), enrollment status (full-time/part-time), relationship status (married/single), work status (employed/unemployed), housing status (on-campus/off-campus), US citizenship (US citizen/non-US citizen), years in the US, distance/online student, fraternity/sorority member, on financial aid, estimated student debt owed, knowledge of campus food bank, and ever accessed the campus food bank. Sociodemographic characteristics were compared based on food security status using chi-square (Categorization of participants based on the individual total scores on the USDA Food Security Short Form determined their food security status (0–1 = “food-secure”, ≥2 = “food-insecure”). Food security status differences were calculated using | PMC10005036 | ||
3. Results | PMC10005036 | |||
3.1. Sample Characteristics | The mean age for college students was 20.77 ± 3.43. Most (78.1%, | PMC10005036 | ||
3.1.1. Food Insecurity | Students responded to a total of six items to measure their individual level of food insecurity. Out of 1087 total responses to all items, 36.1% (Food insecurity differences were found based on race. Most of the students that were food-insecure were non-white (59.6%, | PMC10005036 | ||
3.1.2. Food Insecurity and Food Bank Usage | Over half of all college students (56.5%, | PMC10005036 | ||
3.1.3. Campus Meal Plan | Most students did not have a campus meal plan (52.3%, | PMC10005036 | ||
3.1.4. GPA | The average GPA was 3.45 ± 0.39 for all participants. The mean GPA of all food-insecure students was 3.36 ± 0.42. Significant differences in GPA were found based on food security status (t(1039) = 34.78; | PMC10005036 | ||
3.1.5. Student Employment and Volunteer Activities | Most students (53.7%, College students reported that they participated in volunteer activities for on average 8.64 ± 6.63 h per week. Food-insecure students were significantly more likely to participate in volunteer activities compared to food-secure students (9.42 ± 6.96 vs. 7.58 ± 6.03; t(719) = 13.73; | PMC10005036 | ||
3.1.6. Student Financial Aid and Debt | Most students were currently receiving financial aid (65.1%, | PMC10005036 | ||
3.1.7. Marital Status | Most students were single (97.4%, | PMC10005036 | ||
3.1.8. US Citizenship | Most students were born in the US (86.0%, | PMC10005036 | ||
3.1.9. First-Generation Student | Most (67.3%, | PMC10005036 | ||
3.1.10. Online/Distance Education | Most students were not in an online/distance education program (94.9%, | PMC10005036 | ||
3.1.11. Current Residence | Most students lived off-campus (74.2%, | PMC10005036 | ||
3.1.12. Fraternity/Sorority | Most students were not members of a fraternity/sorority (80.9%, | PMC10005036 | ||
3.2. Food Insecurity and Government Assistance | Participants were asked to “choose all that apply” based on five government assistance programs participated in during childhood. Most students never lived in government housing (94.8%, | PMC10005036 | ||
3.3. Food Insecurity and Food Shortage Contacts | Participants were asked to “choose all that apply” based on who they were comfortable telling that they were short on food. Most students reported they were comfortable telling a parent (79.8%, | PMC10005036 | ||
4. Discussion | PMC10005036 | |||
4.1. Prevalence of Food Insecurity | In the current study, 36.1% of college students were found to be food-insecure. The prevalence of food-insecure college students at this university was nearly four times greater compared to the entire state of Florida (36.1% vs. 9.9%), and three times greater than the food-insecure population in Alachua County, Florida (36.1% vs. 13.4%), where the study was conducted [ | PMC10005036 | ||
4.2. Profile of a Food-Insecure College Student | The current study found many differences in the demographics of food-insecure students compared to the total sample. Food-insecure college students in this study were significantly more likely to be non-white, on financial aid, employed, with lower GPAs, and first-generation college students compared to those that were food-secure. Consistent with the previous literature of food-insecure college students, those individuals that were non-white were more likely than white students to face food insecurity at a higher prevalence rate [ | PMC10005036 | ||
4.2.1. Employment among Food-Insecure College Students | Employment status and hours volunteered per week were found to have significant differences in food security status in the current study. Similarly, one study found that college students were more likely to be food-insecure if they were employed while in school and averaged 18 h of work per week [ | PMC10005036 | ||
4.2.2. Off-Campus Food-Insecure College Students’ Food Access | Off-campus students represented most of the college students within the study. Off-campus college students represented three times as many of the food-insecure students within the study compared to on-campus students (75% vs. 25%). Off-campus students could possibly experience food insecurity due to a number of factors. First, these students are less likely to have campus meal plans (33.8% vs. 66.2%), which possibly forces them to spend money that might not otherwise be allocated specifically for food, but rather money for books and other college-related expenses [ | PMC10005036 | ||
4.2.3. Food-Insecure Students’ Food Shortage Contacts | Being a college student often comes with the notion of the “silent struggle”, representing the stereotypical college student that might be short on food or barely surviving on limited options [One Canadian study of food-insecure college students found that nearly 76% received food from a relative or went to a relative’s home for a meal [ | PMC10005036 | ||
Author Contributions | Conceptualization, C.H.II and D.C.S.J.; methodology, C.H.II; software, D.C.S.J.; validation, C.H.II and D.C.S.J.; formal analysis, C.H.II; investigation, C.H.II; resources, D.C.S.J.; data curation, C.H.II; writing—original draft preparation, C.H.II, D.C.S.J. and A.B.; writing—review and editing, C.H.II, D.C.S.J. and A.B. All authors have read and agreed to the published version of the manuscript. | PMC10005036 | ||
Institutional Review Board Statement | The study was conducted in accordance with the Declaration of Helsinki and approved by the Institutional Review Board (or Ethics Committee) of the University of Florida (IRB#201601763, approved 15 September 2016). | PMC10005036 | ||
Informed Consent Statement | Informed consent was obtained from all subjects involved in the study. | PMC10005036 | ||
Data Availability Statement | The data presented in this study are available upon request from the corresponding author. The data are not publicly available due to FERPA regulations. | PMC10005036 | ||
Conflicts of Interest | The authors declare no conflict of interest. | PMC10005036 | ||
References | Student responses to the USDA Food Security Short Form.Chi-square analysis of childhood access to government assistance based on food security status of undergraduate college students.* Chi-square analysis of contacts for college students if experiencing a food shortage, by food security status.* | PMC10005036 | ||
Introduction | ambiguous social situations, depression, anxiety disorder, anxiety | DISEASE | According to the 2016 Psychological Disease Dynamics Survey released by the Ministry of Health and Welfare of Korea, the demographic distribution of the annual prevalence rate of social anxiety disorder in 2016 was 1.0% in the age group of 18–29 years, and this figure is about twice as high as the below 0.5% for other age groups [Cognitive biases such as interpretation and memory bias has been known to be significant and potent risk factors to cause and maintain social anxiety. Many prior studies have shown that people with social anxiety interpret ambiguous social information as a threat [Cognitive Bias Modification of interpretation (CBM-I) aims to positively correct negative biases individuals with anxiety or depression, by providing repetitive computer-based tasks for interpreting ambiguous social situations in a more flexible and positive way [Memory bias is another factor that prolongs social anxiety. Individual with high levels of social anxiety showed the tendency to recall negative or threatening memories more easily than positive ones in social situations [On the other hand, Hirsch and Clark [Summing up the results of prior studies mentioned above, CBM-I can affect memory bias as well as interpretation bias. However, not many studies have examined the interaction between interpretation and memory bias using CBM-I training. Holmes and Mathews [Without limiting cognitive bias to interpretation bias, we examined the effects of CBM-I on interpretation and memory biases. The research hypotheses are as follows:Hypothesis 1. The positive CBM-I training group will show increased happy and decreased sad mood whereas the negative CBM-I training group will show decreased happy and increased sad mood.Hypothesis 2. The positive CBM-I training group will have more positive interpretations than negative ones on neutral scenarios, whereas the negative CBM-I training group will have more negative interpretations than positive ones.Hypothesis 3. The positive CBM-I training group will recall more positive memories than neutral or negative ones after training, whereas the negative CBM-I training group will recall more negative memories than neutral or positive ones. | PMC10653471 |
Method | PMC10653471 | |||
Participants | A total of 114 undergraduate and graduate students completed an online screening survey using the Center for Epidemiological Studies-Depression(CES-D), Social Avoidance Distress Scale (SADS), and Brief version of Fear of Negative Evaluation Scale (B-FNE). Participants who scored under 16 points on the CES-D, < 99 points on the SADS, and < 48 points on the B-FNE ( | PMC10653471 | ||
Questionnaires | PMC10653471 | |||
Center for Epidemiological Studies-Depression (CES-D) | The CES-D [ | PMC10653471 | ||
Social Avoidance and Distress Scale (SADS) | The SADS [ | PMC10653471 | ||
Brief Version of Fear of Negative Evaluation Scale (B-FNE) | The B-FNE [ | PMC10653471 | ||
Visual Analogue Scale (VAS) | pain | To assess participants’ state mood, they were asked to rate their mood before and after the training and the first filler task. The VAS was used as a self-report measure of current emotions and pain. The VAS is a simple tool that allows individuals to rate their subjective state [ | PMC10653471 | |
Six-item short version of the state-trait anxiety inventory (STAI-6) | STAI-6 [ | PMC10653471 | ||
Training | PMC10653471 | |||
Imagery training | Imagery practice of approximately 10 minutes was conducted for the participants before the CBM-I training. Imagery practice was based on Oxford Imagery Generation (OxIGen) used by Blackwell and colleagues [ | PMC10653471 | ||
Cognitive Bias Modification-Interpretation (CBM-I) | CBM-I was structured on interpretation training with imagery used by previous studies [Salemink, Kindt [ | PMC10653471 | ||
Test phase | PMC10653471 | |||
Similarity Rating Task (SRT) | The SRT was used in this study to assess the participants’ interpretation bias after CBM training. The procedure and format of the SRT were based on the previous studies [<Presentation of liberal arts class>You give a presentation in a liberal arts class.After the presentation, some people ask you questions.As you answer the last question, the classroom goes sil__nt.Did you answer the question?[yes or no]In the following recognition phase, each scenario’s title was presented along with four types of sentences in random order: (1) positive sentence related to the scenario (target positive; i.e., The SRT was conducted using the E-prime 2.0 software and displayed on a 16-inch laptop screen with a white background and navy-colored Arial font. Before actual test, participants completed five practice trials. The mean score for each type of the sentence (target positive, target negative, foil positive, and foil negative) was calculated and used in the final analysis. | PMC10653471 | ||
Free recall task | To assess memory bias related to the scenarios, the free recall task used by Joormann, Waugh [The coding format used by Joormann, Waugh [ | PMC10653471 | ||
Past recollection task | MACLEOD | To examine whether there was a bias in not only recalling the scenario but also a memory from the participant’s actual past, the future thinking task used by MacLeod and Byrne [ | PMC10653471 | |
Procedure | SECONDARY | The present study was approved by the authors’ University’s Institutional Review Board (KWNUIRB-2017-05-006-003). Participants were recruited via internet communities and university bulletin boards. When the participants contacted the researchers, they received a preliminary screening survey link to complete. If the participant met the participation criteria, the participant visited the laboratory to complete the secondary screening survey. Finally, if they met the participant criteria, a description of the study was shared and consent was requested. After the participants shared their consent, the experiment was conducted ( | PMC10653471 | |
Flowchart of the experiment for positive and negative CBM-I. | Participants were randomly assigned to either a positive CBM-I group or a negative CBM-I group. They completed the VAS (happiness, sadness) for baseline measurement and participated in approximately 10 min of imagery training, followed by CBM-I training. Subsequently, the VAS and STAI-6 was completed. To minimize the effect of training-based state mood changes on the SRT, participants participated in the first filler task (reading a neutral story) for approximately five minutes. The participants then completed the VAS and STAI-6 once again followed by the SRT. To decrease the vividness of the scenarios, the second filler task (K-WAIS-IV–Digit Span Backward) was carried out for approximately 3 min before completing the free recall task. Finally, the past-recollection task was completed. Finally, participants were debriefed and thanked for taking part in the study. The entire experiment lasted for 2 h. | PMC10653471 | ||
Result | PMC10653471 | |||
General characteristics of groups and test of homogeneity | To investigate between-group differences in demographic variables and pre-test measurements, independent samples | PMC10653471 | ||
The effects of CBM-I on state mood | ’ mood | To examine the effects of CBM-I on participants’ mood states, we conducted a 2 (group: positive CBM-I, negative CBM-I) × 2 (time: Pre, Post) × 2 (valence: happiness, sadness) repeated ANOVA (dependent variables: pre- and post-levels of sadness and happiness). Analyses revealed the three-way interaction of group, time, and valence, | PMC10653471 | |
The effects of CBM-I on state anxiety | anxiety | To examine the effects of CBM-I on participants’ state anxiety, we conducted a 2 (group: positive CBM-I, negative CBM-I) × 2 (time: Pre, Post) repeated ANOVA (dependent variables: pre-post state anxiety measured with STAI-6). Results revealed that there were the main effect of group, | PMC10653471 | |
The effects of CBM-I on interpretation bias | To investigate the effects of CBM-I on interpretation bias (dependent variables: target positive, target negative, foil positive, foil negative), a generalized linear mixed model (GLMM) was conducted to investigate the interactions between group (positive CBM-I, negative CBM-I), sentence type (target, foil) and valence (positive, negative). Analyses revealed that the three-way interaction between group, valence, and sentence type was not significant, | PMC10653471 | ||
Mean and standard deviation in each group for recognition task, free recall task, and past recollection task. | PMC10653471 | |||
GLMM for Effects of CBM-I training on interpretation bias. | SE | Group was coded as 1 for positive-CBM -I and 2 for negative CBM-I; The Sentence was coded as 1 for Target sentence and 2 for Foil sentence; Valence was coded as 1 for positive and 2 for negative. SE = Standard Error. | PMC10653471 | |
The effects of CBM-I on memory bias | PMC10653471 | |||
Free recall task | A generalized linear mixed models (GLMMs) were conducted to investigate whether CBM-I training affected the participants’ memory bias (see the results of the GLMM in | PMC10653471 | ||
Mean of neutral, positive, and negative memory intrusion made by each training group during the recall task. | Error bar represent 1 | PMC10653471 | ||
GLMM for Effects of CBM-I training on free recall and past recollection. | SE | Group was coded as 1 for positive-CBM -I and 2 for negative CBM-I; Valence was coded as 1 for positive, 0 for neutral, and -1 for negative. SE = Standard Error.For content, a significant two-way interaction between group and content valence was observed, The groups also differed in the number of errors: the negative group reported more errors than the positive group, | PMC10653471 | |
Past recollection task | In addition to the memory bias for SRT scenarios, we investigated whether memory bias could be observed in past memories. A generalized linear mixed models(GLMMs) were conducted to investigate whether CBM-I training influenced the participants’ past memories. Specifically, a two-way interaction between group (positive CBM-I, negative CBM-I) and valence (positive, negative, neutral) was examined in terms of the total number of positive, negative, and neutral memories (dependent variables: positive memories, negative memories, neutral memories) [ | PMC10653471 | ||
Discussion | anxiety | The main purpose of the present study was to examine whether experimentally induced interpretation bias would affect biases in memory in Korean samples. In doing this, the present study used CBM scenarios that were auditory-specific and with a focus on social anxiety symptoms. The main results showed that interpretation biases can be induced, and induced interpretation biases resulted in training congruent state mood and memory biases on both free-recall memory and autobiographical memory. These results supported Hirsh and Clark’s [First, the negative group showed a significantly increased negative state mood (sad) compared with the positive group, whereas the positive group showed a significantly increased positive state mood (happy) compared with the negative group. These findings support hypothesis 1 and are in line with prior studies that showed that modification training of interpretation bias affects state mood [Second, the positive group revealed a positive interpretation bias in both the target and foil sentences, whereas the negative group did not show any difference. As such, hypothesis 2 was partially supported. That the positive group displayed positive interpretation bias in both target and foil sentences is consistent with prior studies [There was no effect of CBM-I training on interpretation bias in the negative group. This result is inconsistent with prior studies that showed that negative CBM-I training can induce negative interpretation bias in a single session [Third, we investigated whether modified interpretation bias could also affect biases in memory. Participants in positive group reported more positive intrusions than the negative group in free recall task, whereas negative group reported more negative intrusions than the positive group, which supports prior results [The limitations of present study and suggestions for further research are as follows. First, the CBM-I trainings in the present study were conducted only with female voices. Among the questions asking about social interaction anxiety [Second, we examined the effectiveness of CBM-I training in healthy participants for only one session and could not confirm whether the training effect lasted. Meta-analysis results have shown that training in quasi-clinical groups and increasing the number of sessions can improve the effectiveness of CBM-I [Third, individual differences in memory capacity could not be controlled. Memory capacity may have affected the recall rate of the recall task, and the ability to save the information needed to perform cognitive tasks in a short period of time as well as the functioning capacity to control and manipulate memory may have affected the performance in imagining while listening to scenarios in CBM-I training [Fourth, to verify the effectiveness of the positive CBM-I training program, we included a comparison group as the negative group, but not a neutral training control group or non-trained control group. So, it is difficult to conclude whether the differences between groups are due to the effects of positive or negative CBM-I training, or the differences in changes caused by both. Therefore, to prove that changes in interpretation and memory bias are inherent effects of positive CBM-I training, the effectiveness of CBM-I training should be verified more clearly by comparing with the results of control groups that receive placebo training, such as neutral interpretation training, or with non-trained groups.Finally, we acknowledge that the absence of a baseline assessment of interpretation bias is a limitation of our study. Therefore, we urge caution in interpreting the results regarding the effect of CBM-I training on interpretation bias. Despite the random assignment of participants to the positive and negative training groups, we cannot disregard the possibility that there might have been initial differences in positive and negative interpretation tendencies between the groups. To gain a more comprehensive understanding of the impact of CBM-I training on interpretation biases, future research should consider incorporating baseline measurements. This would enable a clearer assessment of the effectiveness of CBM-I interventions in shaping interpretation biases.Despite these limitations, present study has the following advantages. First, training scenarios were created focusing on social anxiety symptoms. Second, by verifying that a single session CBM-I training affects interpretation, subsequently ongoing and autobiographical memory, the present study provided partial support for the interaction hypothesis proposed by Hirsch and Clark [ | PMC10653471 |
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