Hope has long been considered as an important therapeutic factor in psychology, medicine and nursing. Without hope, humans struggle to survive during challenging times. Hope can affect the thinking habits, emotional regulations, and behaviors of individuals (1). Individuals who are hopeful tend to have more goal-oriented thinking because this thinking style is rewarded by positive consequences (2, 3). Hope may also affect the emotion regulation processes of individuals; Peh et al. (4) conducted a path analysis to investigate whether hope mediates the association between reappraisal and anxiety/depression in patients newly diagnosed with cancer (N = 144). The result of study indicated that a higher level of reappraisal was correlated with lower levels of negative emotions and hope also mediated the linkages between reappraisal and negative emotions. Hope can also affect the behaviors of individuals. Dixson (5) conducted a cluster analysis which yielded three clusters of hope (high, average, and low) in high school and college students (N = 852). Differences among hope clusters were examined across three domains of indicator variables—(engagement, disengagement, and motivation)—associated with success-oriented behavior in school. The study finally revealed that hope could cause individuals to engage in more success-oriented behaviors (5).

Hope may also influence how people strive to achieve goals. Snyder et al. (2) described two different types of hope: dispositional hope and state hope. Dispositional hope is a trait like characteristic of individuals, which supports individuals to feel constantly hopeful. Whereas, state hope referred to current goal directed thinking, which occurred in short periods of time (6). Dispositional hope contained two interrelated cognitive dimensions: agency and pathway. Agency was the determination and commitment of individuals to help them move to the directions of goals (7). Agency thinking was the perceived capability of and motivation for achieving a desired goal. Individuals, who were high in this dimension, tended to empower themselves by self-talk phrases like, “I can do this all day” or, “I am not going to be stopped” (8). Pathway was described as the perceived capacity of individuals to achieve their goals and set up different plans to overcome the obstacles (7). Pathways thinking referred to the act of evaluating the availability of different resources and methods to reach the desired goals (3). Individuals who were considered as high in the pathways dimension tended to be more decisive and confident about creating workable routes for attaining the desired goal (8). In summary, hope was found to be a positive trait which constantly empowers individuals to seek possible pathways to achieve their desired goals (7).

Despite the potential importance of hope in influencing behaviors, it’s measurement in research studies is challenging. Previous measurements of hope are largely based on self-reported questionnaires, which include different scale including but not limited to the Work Hope Scale (9), Social Hope Scale (10), Locus-of-Hope Scale (11), and Dispositional Hope Scale (2). However, these scales have several fundamental problems in their design. Firstly, there is a problem of central tendency bias, which referred to the avoidance of participants to give answers on both extremes of the Likert scale, and the participants tended to respond in the mid-point (12). The second problem with self-reported questionnaires is that respondents’ answers may be heavily influenced by social desirability. Finally, the participants may have misconceptions about the wordings of the questions, since they are perceiving the questionnaires subjectively, which may be different from the original meanings of words used by the designers (13). In addition, the researchers using these measures require training. They are expensive and by nature the self-completed measures lack objectivity as they rely purely on self-report. Whereas bio indexes are objective, quick, cost effective, and non-invasive measurements.

Using bio-indexes to facilitate the assessment process is not new. For example, hospital nurses or physicians frequently use pulse rate, breathing rate, and blood pressure to investigate if individuals are anxious. Apart from these traditionally used bio-indexes, Heart rate variability (HRV) is a relatively new biometric which measures the fluctuation of time intervals between consecutive heartbeats (14). The variability of the heartbeat determines the capacity to the heart of individuals to alter their level of functioning to manage the changing external environment (15).

Previous studies demonstrated that Heart Rate Variability (HRV) could be an indicator of some physical health problems such as myocardial infarction, sepsis, trauma, sleep apnea, chronic fatigue and cardio-respiratory illnesses (16). Laborde et al. (17) proposed the neural pathway involved in the regulation of the autonomic nervous system (ANS) supports adaptive responses to the environment, stressors, and social behaviors (18). The heart’s activities increase in order to support rapid mobilization of metabolic resources which are required to prepare for appropriate actions (e.g., fight or flight response) (19).

Recent research has also revealed that HRV could reflect different psychiatric conditions, such as anxiety disorder, depression, post-traumatic stress disorder, daily mental stress and prolonged work stress (20–26). HRV can also provide an indication of mental wellbeing such as having good life satisfaction, a high level of positive affect and a low level of negative affect (27, 28). In summary, individuals with different ANS activities could have different HRV patterns. Hence, HRV could be used as bio-indexes of different mental health conditions.

Another direction of research on the usage of HRV on mental health was to investigate the correlations of HRV with positive psychological traits (i.e., resilience). Some studies demonstrated that HRV could be the biomarker of resilience, which is usually defined as a trait of individuals that help them adapt and bounce back from traumatic events or adversities, as it reflects the ability of individuals to maintain a stable equilibrium during stressful events (29, 30). Although recent research indicated that HRV could be bio-indexes of resilience, the possibilities of using HRV as the bio-index of other similar positive psychological traits such as hope have not been studied. The purpose of the current pilot study aimed to fill this research gap, which explored the possible correlations between HRV metrics and hope. If significant correlations between HRV metrics and hope were identified, new bio-indexes for hope would be established. As such, HRV would be able to be considered an alternative objective method to measure hope, without the inherent potential limitations of relying solely on self-reported questionnaires.

Polyvagal theory, which was proposed by Porges (18), provided a tenet for this study. According to this theory, the vagus nerve is a brake that actively inhibits the effect of the sympathetic nervous system (SNS) on heart activity to ensure individuals remain calm and relaxed when at rest. However, when individuals encounter stressful situations this vagal brake is rapidly reduced. Once the brake is released, then the heart’s activities increase in order to support rapid mobilization of metabolic resources which are required to prepare for appropriate action (e.g., fight or flight response) (19). Hence, the polyvagal tone could affect the activities of the ANS and the cognitive reappraisal of events and emotion regulations are also linked with cardiac vagal function. Meanwhile, hope could affect the cognitive reappraisal of events and emotional regulation capacities of individuals. Therefore, with regards to Polyvagal theory, hope should be linked with cardiac vagal function, which regulates the activities of ANS. As a result, the individuals’ difference in the level of hope should have effects on the polyvagal tone as well as the ANS, which could be measured by HRV. Hence, people with different levels of hope should have different patterns of HRV metrics. Supplementary Figure 1 illustrates the above-mentioned relationship.

An individual’s level of hope can affect their capacities of emotion regulation and cognitive reappraisal, which impacts ANS activities. Given that alternations of the activities of ANS are revealed by HRV (31), differences in the level of hope should be reflected by the patterns of HRV metrics among different individuals. Thus, the HRV metrics could serve as bio-indexes of hope. In fact, as revealed by the literature review, HRV has been found to be able to serve as a biomarker of resilience, a similar psychological trait to hope. Therefore, the current research addresses the following research questions:

RQ1. To what extent is there a correlational relationship between HRV and the level of hope? RQ2. What is the overall level of hope among Hong Kong Chinese Adults?

This is the first study providing initial evidence to support the potential use of HRV metrics as bio-indexes of hope. The results indicate that there were significant, weak, and negative correlations between most of the HRV metrics (i.e., HF, SDNN, RMSSD, and MIBI) and the level of hope. There was also a trend toward significant negative correlations observed on VLF, TP, and LF of HRV metrics. Hence, individuals with a higher level of hope tended to have a lower level of HRV metrics including HF, SDNN, RMSSD, MIBI, VLF, TP, and LF. The results also revealed that the subscales (Pathway) tend to have more negative correlations with HRV indexes. This result indicates that an individual’s act of evaluating the availability of different resources and methods to reach the desired goals tend to have negative impacts on HRV indexes.

Previous studies revealed that individuals with a lower level of HRV metrics tend to have poor health status, such as immune dysfunction and inflammation, cardiovascular disease, and mortality [Dekker et al. (43); Fang et al. (44); Kemp and Quintana (45)]. In addition, the current study revealed that higher level of hope was positively correlated with higher level of MHR, which was measured at resting status in this study. Higher resting heart rate could be seen as an index of poorer cardiovascular health and cardiovascular disease such as hypertension (46). In other words, positive traits, such as hope, can bring positive mental health outcomes for individuals (47) but they might impose negative effects on physical health in the population being studied.

The phenomenon observed in the present study could be explained by Segerstrom’s (48) Engagement Hypothesis. Segerstrom (48) proposed that individuals with more positive traits (e.g., optimism) tended to have more active engagement to the stressful environment during periods of adversity. These individuals believed that the active engagement with stressors could lead to termination of their stressor by participating in more problem-solving activities. However, if the circumstances were too challenging or demanding, individuals with more positive traits (e.g., optimism) tended to have poorer immunity, because these traits resulted in individuals being continuously focused on the stressful situation in an attempt to resolve the issues (49). In contrast, if the individuals had fewer positive traits (e.g., more pessimistic), they tended to give up more easily, disengage, and avoid active engagement in coping against the adversities. Hence, their physiological functioning, such as level of immunity, was less affected by the extreme environment. One previous study of the relationship between HRV and resilience (a positive trait similar to hope), revealed that the score of resilience was also negatively correlated to HRV metrics (LF and HF) which echoes our findings (29). In addition, An et al. (29) study also reported that higher scores in resilience were significantly and positively correlated with more adaptive engagement to adverse situations, i.e., an individuals’ tendency to actively participate in more adaptive behaviors during a challenging time (29). In other words, individuals with more positive traits (e.g., hope, optimism, and resilience) could have greater engagement with stressors during extreme situations, resulting in higher levels of demand on physiological functioning. In contrast, participants with lower levels of hope tended to have less engagement with adverse situations, resulting in fewer demands on physiological functioning. Even though people with positive traits were more likely to handle a higher level of stress and were apparently not distressed by it, stress could be reflected by objective measurements like HRV. The negative associations between HRV metrics and hope observed in the current study may have been due to participants’ active engagement with the stressful environment during COVID-19. During this study, the research participants were residing in Hong Kong during the COVID-19 pandemic; threats of infections, mandatory quarantine, lockdowns, and layoffs were seriously affecting the metropolitan. Most of the Hong Kong citizens were suffering from a greater level of stress (i.e., increased by 28.3%) and anxiety (i.e., increased by 42.3%), and the depression symptoms and unhappiness have doubled since COVID-19 outbreak when compared to 2016 and 2017 (50). With that premise, the Engagement Hypothesis could be applied, because the environments became demanding and challenging as evidenced by the elevated stress among the Hong Kong people.

As indicated in the Engagement Hypothesis, individuals, who possessed more of these positive traits (e.g., hope, optimism, and resilience), tended to be more engaged in the stressful environments. This difference was because these individuals tended to believe that they could tackle the stressors. The more frequent exposure to stressors may have resulted in a higher level of stress and extra physiological costs, which were captured by the reduced HRV metrics. Hence, negative and significant correlations were observed between the HRV metrics and hope in the current study.

The results of current study provide initial evidence to support the use of HRV as a bio-index of hope. If researchers are able to generate more evidence which supports the linkage between HRV and hope in subsequent larger studies, then clinicians should consider using HRV metrics to assess the level of hope of their clients during clinical practice or/and research, alongside the self-report questionnaire. This approach could increase the objectivity of psychological evaluations and result in more reliable research outcomes. Measuring HRV is also cost effective, efficient, non-invasive, and straightforward.

The second implication for professional practice was that it alleviated the problem of assessment and re-assessment period of questionnaire-based clinical evaluation on the level of hope. In many questionnaire-based psychometric tests, time intervals between questionnaire administrations are required. Some researchers suggest that this time interval should range from 2 days to 2 weeks (51). Therefore, frontline psychologists had to wait until the next session to review the progress of their intervention. This problem also applied to the measurements of hope as they traditionally rely on questionnaire-based assessment. As the results indicated that HRV metrics have the potential to be a bio-index of this trait, the frontline psychologists could use the HRV metrics to evaluate and re-evaluate the level of hope within the same session. This change of practice provided a more direct and instant way for the frontline psychologists to review the effectiveness of their treatment. However, this second implication is subjected to the results of future study.

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

The studies involving human participants were reviewed and approved by the California Southern University. The patients/participants provided their written informed consent to participate in this study.

YH and WW contributed to the conception and design of the study. YH and MT organized the database. YH performed the statistical analysis. YH, WW, and DB wrote the first draft of the manuscript. YH, YL, MT, HP, DB, and WW wrote sections of the manuscript. All authors contributed to the manuscript revision, read, and approved the submitted version.