Adherence to hunger training using blood glucose monitoring: a feasibility study

M R Jospe
R C Brown
M Roy
R W Taylor
Adherence to hunger training using blood glucose monitoring: a feasibility study
10.1186/s12986-015-0017-2M E T H O D O L O G Y Open Access
Background: "Hunger training", which aims to teach people to eat only when blood glucose is below a set target, appears promising as a weight loss strategy. As the ability of participants to adhere to the rigorous protocol has been insufficiently described, we sought to determine the feasibility of hunger training, in terms of retention in the study, adherence to measuring blood glucose, and eating only when blood glucose concentrations are below a set level of 4.7 mmol/L. Method: We undertook a two-week feasibility study, utilising an adaptive design approach where the specific blood glucose cut-off was the adaptive feature. A blood glucose cut-off of 4.7 mmol/L (protocol A) was used for the first 20 participants. A priori we decided that if interim analysis revealed that this cut-off did not meet our feasibility criteria, the remaining ten participants would use an individualised cut-off based on their fasting glucose concentrations (protocol B). Results: Retention of the participants in the study was 97 % (28/29 participants), achieving our criterion of 85 %. Participants measured their blood glucose before 94 % (95 % CI 91, 98) of eating occasions (criterion 80 %). However, participants following protocol A, which used a standard blood glucose cut-off of 4.7 mmol/L, were only able to adhere to eating when blood glucose was below the prescribed level 66 % of the time, below our within-person criterion of 75 %. By contrast, those participants following protocol B (individualised cut-off) adhered to the eating protocol 84 % of the time, a significant (p = 0.010) improvement over protocol A. Conclusion: Hunger training appears to be a feasible method, at least in the short-term, when an individualised fasting blood glucose is used to indicate that a meal can begin.

Background

The persistent obesity epidemic has generated a plethora of weight loss studies that investigate the effectiveness of diets varying in macronutrient recommendations. However, it appears that modifying the composition of diets has a minor impact on weight loss, especially over six months or longer [1][2][3][4][5]. Instead, a far more relevant factor appears to be the degree of adherence to the prescribed diet [1,6,7], which may be encouraged by behavioural strategies [8].

One effective strategy for weight loss may be learning to eat only when hungry, as eating in response to environmental, social, or emotional cues rather than physical hunger has been consistently associated with a higher body mass index (BMI) and energy intake [9][10][11]. Ciampolini et al. [12] have developed an intriguing protocol that shows promise in training people to eat according to their hunger. Participants are trained to connect their physical symptoms of hunger with their blood glucose, and to eat only when their blood glucose is below a set target of 4.7 mmol/L. When tested in a group of 74 overweight participants, this method of "hunger training" (or "hunger recognition", as coined by Ciampolini et al. [12]) produced significantly greater weight loss over 5 months (3.5 kg), compared with that observed following the conventional approach of increasing vegetable intake and physical activity [13].

While these initial results appear promising, replication by another group would provide further support for the use of this weight management strategy. The upcoming SWIFT trial will test the effectiveness of four behavioural strategies, including hunger training, on adherence to diets and the resulting weight loss in overweight adults during a two-year randomised controlled trial (RCT) [14]. However, we believed there were several important considerations regarding the feasibility of the hunger training protocol that required addressing before we could incorporate it into our larger trial. Firstly, adherence to the protocol does not appear to have been reported, but is critical given that even those who need to self-monitor their blood glucose for diabetes management struggle to do so even once per day [15]. Secondly, some concern was raised regarding the suitability of 4.7 mmol/L as a standard cut-off for all participants given that this is below the fasting glucose level for the majority of non-diabetic adults [16,17]. Lastly, there was some doubt over whether blood glucose is an appropriate measure of perceived hunger, given that different studies have indicated that blood glucose and hunger are not correlated [18], whereas others [19] have reported significant correlations as high as r = 0.55-0.63.

Given our concerns, we decided to undertake a feasibility study [20] before including hunger training as a support strategy in the larger SWIFT trial. We sought to answer questions about adherence, the use of 4.7 mmol/L as a cut-off, and the appropriateness of using blood glucose to indicate hunger. We used an adaptive design method [21], with the blood glucose cut-off as the adaptive feature [22] to increase our chance of a finding a viable method for hunger training. Adaptive design trials are formulated to allow modification to the study while it is underway, in order to efficiently learn from the data being gathered. A priori rules guide the modification of the adaptive feature (the characteristic of the protocol and study which may require modification), and decisions are made based on cumulative data from interim analyses [21,22]. For this study, we decided a priori to analyse the adherence data to the original protocol (protocol A) after 20 participants, before deciding on modifications to the blood glucose cutoff based on predefined feasibility criteria.

The aim of our single-centre, single-arm study was to determine the feasibility of the hunger training protocol [12]-with particular attention to study retention, adherence of measuring blood glucose, and adherence to eating below the blood glucose cut-off-to inform the design and use of hunger training within the large randomised controlled SWIFT trial [14].

Methods

Participants and setting

Participants were recruited by advertisement and word of mouth in Dunedin, New Zealand. Eligible participants of both sexes had to be at least 18 years of age. We recruited both normal weight and overweight participants, aiming for a final ratio of 2:1 (overweight: normal weight). While only overweight participants will be included in the main SWIFT trial, normal weight participants were included in this feasibility study primarily to determine whether the results vary with BMI and whether hunger training might be suitable for weight maintenance in normal weight participants, as was suggested in the original protocol [13]. Exclusion criteria included having type 1 or type 2 diabetes or heart disease. Participants had to be willing to measure their blood glucose via finger prick test three to eight times per day, and to wear a continuous glucose monitoring system if required. Participants were informed about the experimental feasibility design before written informed consent was obtained. The study was approved by the University of Otago Human Ethics Committee (H14/076).

Intervention

Participants followed the hunger training procedure for two weeks, which was based on Ciampolini et al. [13]. Participants were provided with a booklet in which to record hunger levels, blood glucose values, and food consumed ( Fig. 1). Every time a participant wanted to eat, they were instructed to assess their hunger level using a visual analogue scale ranging from 0 mm (not at all hungry) to 100 mm (extremely hungry) [23,24]. Once that was completed, participants were instructed to measure capillary blood glucose from a finger prick sample by portable glucometer (Abbott Freestyle Optium Glucose Meter, Australia). Participants were only permitted to eat if their blood glucose was under their specified cut-off. If their blood glucose was above their cut-off, they were instructed to choose an activity that distracted them from food, and to wait for new feelings of hunger for at least an hour before testing their blood glucose again (Fig. 2). Participants were permitted to consume hypocaloric drinks at any time. Participants were also specifically instructed that they could only drink alcohol if their blood glucose was below their cut-off.

We suggested that participants eat primarily fruit and vegetables for the first few days of hunger training as per Ciampolini et al. [13] in order to experience hunger more frequently and therefore become more familiar with the symptoms of hunger. As the training progressed, we encouraged participants to gradually reintroduce their typical foods and pay attention to the effect on hunger and blood glucose.

Blood glucose Cut-off

To test the appropriateness and feasibility of the blood glucose cut-off, we used an adaptive design approach [21], with blood glucose cut-off as the adaptive feature [22]. A priori we decided to initially use 4.7 mmol/L as the cut-off (protocol A) as per the original protocol [12] and to undertake an interim analysis after the first 20 participants had completed the training. If this analysis indicated that the average within-person proportion of eating occasions where blood glucose was below the cutoff was more than 75 %, the cut-off would be individualised following protocol B.

Protocol A

All participants in cohort A could only eat if their blood glucose was 4.7 mmol/L or less.

Protocol B

Each participant in cohort B had an individual blood glucose cut-off, which was calculated as the average of the fasting glucose of the first two days of hunger training. If a participant's individualised cut-off was less than 4.7 mmol/L, they were given a cut-off of 4.7 mmol/L. During the first two days of hunger training, participants used their fasting glucose of that morning for their cutoff for the day.

Procedure

Participants met with the researcher on three occasions over two weeks (baseline, day 7 and day 14). At the first visit, height was measured with a fixed stadiometer (Heightronic, QuickMedical, WA, USA) and weight by electronic scales (Tanita BC-418). Participants completed a brief questionnaire that included demographic information [25], the self-administered short form of the International Physical Activity Questionnaire (IPAQ) [26], and the Intuitive Eating Scale-2 [27]. Participants were introduced to hunger training and taught to measure their blood glucose. After the first day of hunger training, the researcher telephoned the participant to ensure that the instructions were understood and able to be followed, and to answer any questions. At the second visit participants had the opportunity to ask questions and talk about any challenges or successes. If participants had difficulty only eating when their blood glucose was 4.7 mmol/L or less in the first week, they wore a continuous glucose monitoring system (CGMS) (iPro2 Continuous Glucose Monitoring System, Medtronic, California, USA) for the second week of the study. The CGMS allowed us to observe blood glucose variation in greater detail than the intermittent finger prick glucose tests. The sensor was inserted subcutaneously into the abdominal area and monitored glucose in the interstitial fluid every five minutes for seven days. On the last visit, all participants had their weight measured again, and were asked about their experience of the study during a semi-structured interview, which was recorded (Philips Voice Tracer digital recorder LFH0622) and transcribed. The exit interviews were examined with a data-driven thematic analysis.

Feasibility criteria

A priori we specified that hunger training would be considered feasible if all of the following criteria were met: 1. 85 % or more of participants completed the study. 2. The average within-person proportion of eating occasions where blood glucose was measured was more than 80 %. 3. The average within-person proportion of eating occasions where blood glucose was below the cut-off was more than 75 %.

We were also interested in examining the within-person correlation between hunger and blood glucose, and body

Analyses

Descriptive statistics were used to answer the feasibility criteria, with adherence measures and the correlation between hunger and blood glucose calculated for each participant before generating within-person point and interval estimates. Study completion was calculated as the number of participants who attended the final visit at day 14 divided by the number of participants who attended the first visit. Adherence to measuring blood glucose was calculated by dividing the number of reported eating occasions where blood glucose was noted by the total number of eating occasions for each participant. Adherence to eating when below the specified blood glucose cut-off was calculated by dividing the number of reported eating reported eating occasions where blood glucose was below the assigned cut-off by the total number of eating occasions with a noted blood glucose value. Differences between groups were analysed using t-tests, with unequal variances where indicated. Paired t-tests were used to compare the average of the first two days of fasting glucose with fasting glucose for the 14-day period.

As no formal power calculations are undertaken in feasibility studies, sample sizes should be based on estimating feasibility outcomes (Arain, Campbell, Cooper, & Lancaster, 2010). We estimated that a minimum of 25 participants was appropriate to estimate the retention rate and adherence to the hunger training intervention. Statistical analysis was performed using Stata 12 (StataCorp, College Station, TX).

Results

Recruitment took place on July 2, 2014 by sending an invitation email to staff and students at the University of Otago in Dunedin, New Zealand. All participants were recruited in a single day. The pilot study took place from July 8 to Aug 6, 2014 and 30 participants were recruited. One participant discontinued hunger training due to fasting glucose levels being above the cut-off for possible diabetes diagnosis (>7 mmol/L), resulting in 29 participants being included in the final analysis.

The participants were predominantly well-educated, white women, with an average BMI of 31 kg/m 2 ( Table 1). As there were no significant differences in demographic characteristics at baseline between cohorts A and B (data not shown), only the combined results are presented.

Adherence

Adherence was measured in terms of overall study retention, glucose measurement prior to eating, and compliance to the blood glucose cut-off. There was no difference in any adherence measures between lean and overweight participants (data not shown). Retention was high, with 28 out of 29 participants (97 %) completing the pilot study, well above our predetermined criterion for determining success of 85 %.

In terms of adherence to the blood testing protocol, the within-person proportion for measuring blood glucose before eating was 94 % (95 % CI 91, 98) of eating occasions, with no significant difference between cohorts A and B. This level of adherence considerably exceeded our a priori requirement of measuring before 80 % of eating occasions.

Adherence to eating with protocol A

Participants following protocol A, which used a universal blood glucose cut-off of 4.7 mmol/L, adhered to the goal of eating only when blood glucose was below this level 66 % of the time, which was below our withinperson adherence requirement of 75 % ( Table 2). Furthermore, four out of the nineteen (21 %) participants in this cohort adhered to protocol A on less than half of their eating occasions (Fig. 3), with one participant only adhering on two eating occasions over the two week period (5 % of all eating occasions).

Of these 19 participants, six who appeared to be struggling with only eating when their blood glucose was 4.7 mmol/L or less during the first week of training wore a continuous glucose measurement sensor during week two. On average, their blood glucose was above the protocol A cut-off of 4.7 mmol/L 85 % of the time over the seven days, or 143 hours of the total 168 hours captured. Additionally, five of these six participants had at least one day where their blood glucose never dropped below 4.7 mmol/L. Fig. 4 illustrates the results from one of these participants, showing their blood glucose over 7 days and the amount of time it was under both protocol A (universal 4.7 mmol/L cutoff) and protocol B (individualised cutoff of 6.2 mmol/L for this participant).

Adherence to eating with protocol B

A priori we decided that the blood glucose cut-off would be deemed feasible if the within-person proportion of measured blood glucose was below the cut-off on more than 75 % of eating occasions. As protocol A was below this requirement, we switched to protocol B for the remaining ten participants. For protocol B participants, their individualised blood glucose cut-off was calculated from the average of fasting glucose over the first two days of hunger training, which resulted in an average cut-off of 5.5 mmol/L (SD 3.0). The use of this individualised protocol increased adherence to the eating protocol to a within-person proportion of 84 % of the time, which was a significant increase over protocol A (p = 0.010) and met our benchmark for feasibility. We examined whether the average of the first two days of fasting glucose was a suitable estimate of the average fasting glucose throughout the two-week period, and therefore was suitable to determine the individualised cut-off. Fasting glucose means were comparable (difference 0.07 mmol/L), with no significant difference between these time periods (p = 0.415).

Correlation between hunger and blood glucose

A significant, albeit modest, inverse within-person correlation was observed between perceived hunger and blood glucose concentrations of r = −0.23 (95 % CI −0.15, −0.31), p < 0.001. Cohort B tended to have a stronger correlation between hunger and glucose (r = −0.33 for cohort B vs r = −0.18 for cohort A) although our small numbers probably preclude statistical significance (p = 0.082).

Weight loss

Overweight participants following both protocols achieved significant weight loss over the two-week period, with an average loss of 1.5 kg (95 % CI 2.2, 0.9) and a corresponding reduction in BMI of 0.6 kg/m 2 (95 % CI 0.3, 0.8), p < 0.001 (Table 3). By contrast, lean participants maintained  their weight (p = 0.337, data not shown). Although participants following protocol B appeared to lose more weight than those in protocol A, differences were not significant ( Table 3).

Exit interviews

The major themes that emerged from the exit interviews were: awareness of non-hungry eating, change of dietary intake, and the use of blood glucose monitoring.

Awareness of non-hungry eating

Participants reflected that hunger training made them realise that they were used to eating for reasons other than hunger, including as a way to stave off boredom, to cope with emotions, and because of habit: Many participants chose better food, because they were more conscious of their food intake or because they noticed how their blood glucose reacted afterwards:
It
My lunches I've changed totally. It's now three mandarins, a pottle of yoghurt, and a banana. And before? It was anything that I wanted. I'm not eating things like chips and crackers. Now it's a treat. It's really changed how much processed food and I'm definitely eating much more vegetables, and enjoying them. It just seems like a waste now to eat a bag of chips, since then I can't have dinner.

Many participants reduced their portion sizes at meals, and noted that they were surprised that they didn't need as much food as they had previously assumed, and could no longer imagine eating as much as before the pilot:

I still served myself the same portions but I couldn't eat it all. Which is kind of weird for me. I stopped when I had enough. I don't know if I'm just getting used to eating a bit less or I realise that I didn't NEED to eat it all. Whereas before I would finish it because it was wasteful. But now I think, "you don't have to force yourself to eat stuff that you don't need to." And that rating of fullness at the end was quite good, because before I would have that too full, gross, feeling. But now thinking about what full feels like and overfull feels like and what not quite full feels like.

Cutting down on what I was having for breakfast did make a difference to my blood sugar before lunchtime. So just having porridge OR toast rather than porridge AND toast meant that I could have lunch at lunchtime.  Conversely, a few participants reported that their portion sizes increased, since they were hungrier than usual because they were eating less frequently:

But when I was hungry, I ate MORE. Because I was having to wait until lunchtimes until I could eat, so when I got lunch, I ate more than I normally would have. If I hadn't eaten until 1 pm, anything that wasn't tied down wasn't safe! Once I pricked my finger, I would have my meal and then the chocolate, because I better have the chocolate now else I'll have to stab my finger again. It eliminates the grazing but increased my portion sizes.

The use of blood glucose monitoring

Many participants commented that their blood glucose was an unpredictable measure of their hunger:

I was having trouble with the [glucose] readings and matching it up with what I was doing, but it certainly made me think about registering whether I was hungry rather than just eating because of routine. Super hungry didn't seem to corresponding to particularly low glucose. I did find it frustratingly inaccurate in terms of measuring my hunger, even though I was much more in touch with my hunger.

However, most participants viewed measuring their blood glucose as a useful behaviour for gaining awareness of their eating habits:

It's not just the fact that you inflict pain on yourself it's the fact that you inflict pain and it might say "no" anyway. Really have to think, "look, am I actually feeling hungry enough?". I think it's extremely effective since it just makes you more aware. Even if I didn't hurt, it makes you aware of those wee niggle, but "I've only eating a little awhile ago". The pain element is useful and the fear of the rejection after the pain. The psychological thing of having to prick your finger every time you want to eat is a bit of a red herring but it's quite a relevant thing. I think "hmm, I would like some afternoon tea but my fingers are a bit sore today…maybe I don't need it"

Many participants commented that the pain with finger pricking reduced after the first week:

I have the feeling that stabbing gets better. At the beginning it hurt more, but I really can't feel it anymore. I don't find it particularly onerous. It's easy enough to fit and only a minor irritation, to prick your finger.

Discussion

The idea that using finger prick blood glucose monitoring to train individuals to eat only when hungry appeared to be a promising method for weight management, based on the results from a group of researchers [12]. However, neither this original study, nor an offshoot from these authors [28] reported on adherence, a critical consideration for determining success. Thus we repeated the Ciampolini protocol (protocol A) using an adaptive design, which allowed us to determine if an alternative blood glucose cutoff might be more suitable. In fact, we found that only our adaptive version of hunger training (protocol B) was feasible, in terms of meeting all three criteria: study retention, adherence to measuring blood glucose, and adherence to eating below the blood glucose cut-off. It is difficult to compare our findings as neither of the previous studies reported on feasibility per se. However, both previous studies reported retention of 80 % of participants [13,28], which is likely comparable to our retention rates, considering their longer duration.

We anticipated that adherence to measuring blood glucose prior to every eating occasion would be an issue, however this did not seem to be the case. Participants measured their blood glucose before nearly every meal over this two-week trial. Our qualitative data suggests that participants thought that the pain and hassle of pricking their fingertips and measuring their blood glucose was not as bad as they had originally expected. Although our adherence to measuring blood glucose was considerably higher than that reported with diabetics [15], this is likely to be influenced by the short-term nature of our trial.

While both the original (protocol A) and adapted (protocol B) protocols achieved our criteria for success of feasibility in terms of retention and adherence to measuring blood glucose, only the protocol B met the criterion for adherence to eating below the blood glucose cut-off. Participants' difficulty of adhering to only eating when their blood glucose was under 4.7 mmol/L is consistent with large surveys that show that this concentration is below the fasting glucose level for the majority of non-diabetic adults [16,17]. Thus adapting our protocol to use an individualised blood glucose cut-off substantially improved adherence to only eating when blood glucose was below the cut-off. Furthermore, this adaptation might improve weight loss success as well, at least over the short-term. Our overweight participants lost 1.5 kg on average over the fortnight, with those following protocol B losing nearly twice as much weight compared with protocol (although this difference did not reach statistical significance). This finding was intriguing given we anticipated that the more lenient cut-off (protocol B) would diminish rather than enhance weight loss. However, both the diary information and the accompanying qualitative interviews showed that protocol B was more successful at encouraging participants to only eat when hungry. One limitation of our data is that although we requested brief dietary information as part of the two-week diary, this was not detailed enough to allow energy intake to be calculated. Thus exactly how this more lenient cut-off changed dietary intake is uncertain. Furthermore our trial only lasted two weeks and longer follow-up is required to determine whether hunger training offers a viable method for sustained weight loss. As hunger training will be one support strategy in the upcoming SWIFT trial, we will have the opportunity to examine the effectiveness of hunger training on weight loss over two years. However, our findings do support those of Ciampolini et al. who demonstrated an average weight loss of 5.8 kg over 5 months in overweight participants [13].

Our findings of only a weak correlation between hunger and blood glucose are in agreement with the range of results reported in the literature [18,19]. The weak and inconsistent association between hunger and blood glucose was the most problematic element for participants, as evident in their feedback. However, most participants felt that measuring blood glucose still provided valuable feedback and was crucial for modifying their eating behaviour, irrespective of its limitations. It seems as if the benefits of objective biofeedback provided by measuring their blood glucose levels enhances the benefits of monitoring appetite [29,30] and dietary intake [31]. Similarly, self-monitoring of blood glucose in diabetics has been shown to improve adherence to nutritional recommendations and decrease body weight [32]. Furthermore, the necessity to measure blood glucose concentrations required participants to record their appetite and blood glucose results at that instant, rather than hours later, which may be beneficial as a shorter time interval between eating and dietary selfmonitoring has been shown to be significantly associated with weight loss [33].

The main strength of this feasibility study is our careful measure of adherence, which has not previously been described for this intriguing weight management strategy. Feasibility and pilot studies are an important step before a large trial, and are especially useful when feasibility objectives and success criteria are defined a priori [34]. Including an adaptive design approach helped us to efficiently find the most suitable blood glucose cut-off for our participants, which appears to be an improvement over the original method. This study provided us with confidence about the feasibility of the hunger training method in general, and knowledge that an individualised blood glucose cut-off is the most viable approach. Hunger training may provide an effective strategy for weight loss by teaching people to eat according to their physical hunger rather than in response to environmental, social, or emotional cues. Based on our findings, the SWIFT trial will include our adapted version of hunger training as one of four intervention arms [14]. However, our study also has some limitations. As befitting a feasibility study, it was relatively short, but was based on the original protocol that showed the majority of participants were trained after two weeks. While it would have been interesting to use continuous monitoring in all participants, it was cost prohibitive. However, with the advancements in non-invasive glucose monitoring, such as using contact lenses [35] and temporary tattoos [36], continuous (and pain-free) blood glucose monitoring is likely to become more affordable and accessible.

Conclusions

Our results show that participants are willing to participate in hunger training, measure their glucose before eating, and only eat when their blood glucose is under their individualised cut-off, at least in the short-term. The results of our study suggest that the adapted version of the hunger training protocol is feasible, and viable to use as a support strategy in the SWIFT trial. Further testing of hunger training in the SWIFT trial will allow us to examine the effects of this method over a two-year duration in combination with healthy eating and exercise advice.

Fig. 1
1The example page in the hunger training booklet

Fig. 2
2Hunger training instructions in the hunger training booklet weight change (if any) in overweight participants over the two weeks.

Fig. 3
3Histogram of adherence to eating below the blood glucose cut-off in cohort A and cohort B

Fig. 4
4Results from the continuous glucose monitoring sensor from a participant over 7 days, comparing the amount and percentage of time the participant was below the protocol A cut-off of 4.7 mmol/L and protocol B individualised cut-off of 6.2 mmol/L Table 3 Change in weight and BMI over 2 weeks in overweight participants Variable Cohort A (N = 13) Cohort B (N = 9) p-value Weight change (kg) −1.1 (−0.5, −1.8) −2.1 (−0.6, −3.6) 0.134 BMI change (kg/m 2 ) −0.4 (−0.2, −0.6) −0.8 (−0.2, −1.4) 0.137 Data presented as mean (95 % CI)

Table 1
1Characteristics of participantsVariable
All (N = 29)

Age (years)
43.3 ± 12.5

Height (m)
1.7 ± 0.1

Weight (kg)
89.8 ± 25.2

Body mass index (kg/m 2 )
31.2 ± 9.0

Women, n (%)
22 (76 %)

White ethnicity, n (%)
27 (93 %)

Partnership status, n (%)

Partnered
16 (55 %)

Non-partnered
13 (45 %)

University degree, n (%)
19 (66 %)

Intuitive eating questionnaire

Overall score a
3.1 ± 0.5

Unconditional permission to eat
3.3 ± 0.6

Eating for physical rather than emotional reasons
2.9 ± 0.7

Reliance on hunger
2.9 ± 0.7

Body-food choice congruence
3.4 ± 0.9

Physical activity (MET-minutes/week) b
2019 ± 2955

Hours of sitting per day b
7.1 ± 2.6

Note: Unless indicated, values are mean ± SD

a

The intuitive eating score ranges from one to five, with higher scores
indicating greater levels of intuitive eating

b

median ± IQR

Table 2
2Adherence to hunger trainingAdherence
Cohort A (N = 19)
Cohort B (N = 10)
p-value

Measuring glucose
92.3 % (88.6, 96.0)
96.1 % (93.9, 98.2)
0.149

Eating below glucose cut-off
66.4 % (54.2, 78.6)
84.4 % (78.0, 90.8)
0.010

Data presented as within-person mean (95 % CI)

AcknowledgementsWe wish to thank the University of Otago for funding, Dr Jill Haszard and Dr Josie Athens for statistical advice, and all the participants who volunteered their time and fingertips to this study.Abbreviations BMI: Body mass index; CGMS: Continuous glucose monitoring system; CI: Confidence interval; IQR: Interquartile range; MET: Metabolic equivalent of task; SD: Standard deviation.Competing interestsThe authors declare that they have no competing interests.Authors' contributions MJ carried out the feasibility study, including all participant interactions, analysed the data, and drafted the manuscript. RB participated in the design and coordination of the study and helped to draft the manuscript. MR participated in the design of the study, provided medical oversight, and helped to draft the manuscript. RT conceived of the study, and participated in the design and coordination and helped to draft the manuscript. All of the authors have been involved in the writing of this manuscript and have read and approved the final text.
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Which deliberate self-poisoning patients are most likely to make high-lethality suicide attempts?
2015

Sang Hoon Oh
Han Joon Kim
Soo Hyun Kim
Young Min Kim
Kyu Nam Park
Which deliberate self-poisoning patients are most likely to make high-lethality suicide attempts?

Int J Ment Health Syst
935201510.1186/s13033-015-0028-4RESEARCHDeliberate self-poisoningRisk factorsRescue factorsSuicide attempts
Background:The risk/rescue rating scale (RRRS) assesses the lethality of a suicide attempt, which is defined as the probability of inflicting irreversible damage. We assessed the lethality of suicide attempts using the RRRS and identified the socio-demographic profiles of patients who achieved high lethality in deliberate self-poisoning (DSP).Methods:A retrospective study was conducted to evaluate DSP patients who visited the emergency department of a tertiary teaching hospital between 2000 and 2011. The data included socio-demographic information, clinical variables, risk factors (e.g., the method used, whether consciousness was impaired, toxicity, reversibility and whether treatment was required) and rescue factors (e.g., location, who initiated the rescue, the probability of discovery, the accessibility of rescue, and delay until discovery). The high-risk group consisted of patients with 11-15 risk points, whereas patients in the low-rescue group had 5-11 risk points. We examined the characteristics of high-lethality suicide attempts (high-risk/low-rescue group).Results: A total of 1114 patients were enrolled in this study. Pearson's correlation analysis showed that the total risk score for patients with DSP was negatively associated with the total rescue score (r = −0.201, p < 0.001). Of the total number of DSP patients, 42 were included in the high-risk/low-rescue group. The multivariate logistic regression analyses showed significant associations between high-lethality suicide attempts and male gender (OR 2.70, 95 % CI 1.41-5.18, p = 0.003), older age (OR 1.02, 95 % CI 1.01-1.04, p = 0.015), and unemployment (OR 2.98, 95 % CI 1.41-6.33, p = 0.004).Conclusion:This retrospective study demonstrates that male gender, advanced age, and unemployed status were associated with high-lethality suicide attempts in DSP patients.

Background

Suicide is one of the most serious and urgent public health issues in South Korea [1]. South Korea has the highest suicide rate among member countries of the Organization for Economic Cooperation and Development, and suicide was the fourth leading cause of death following cancer, stroke, and cardiovascular disease in 2011 [1].

Attempted suicide is known to increase the risk of subsequent death through suicide [2]. Suicide attempts have been reported to occur 10-20 times more frequently than completed suicides [3]. Different studies have shown that the lifetime prevalence in different countries varies widely, ranging from 1.3 to 6.2 % [4,5]. Among various suicide methods, deliberate self-poisoning (DSP) is typically classified as a low-lethality method [6,7], but the majority of self-harm episodes involve poisoning [8]. The risk of subsequent suicide after the first presentation of DSP is more than 40-fold higher in DSP patients compared to population-based controls [9]. Furthermore, one study assessing suicide methods showed that DSP is the most commonly used suicide method in different countries [10].

Suicidal intent refers to the patient's subjective expectation and desire to die as a result of a self-inflicted injury [11]. However, this expectation sometimes may not correspond to the lethality of an attempt. Some people harm themselves deliberately without suicidal intent. Therefore, it is difficult to predict the risk of a completed suicide based on suicidal intent alone. Suicide risk assessment is a complex process involving the consideration of many factors and contexts, and two separate dimensions must be assessed to identify suicide attempts. These dimensions include the degree of intent to die by suicide and the severity of the self-injury at the time of the attempt. One validated clinical scale, the risk/rescue rating scale (RRRS), was developed to measure the severity of self-injury at the time of the suicide attempt [12]. The RRRS is expressed as a ratio of factors influencing risk and rescue, and the ratio of risk to rescue may be a balance of calculated factors related to the degree of irreversible damage. Of the possible combinations of risk and rescue, those patients in the high-risk/low-rescue group showed suicide attempts with the highest lethality. Kim et al. reported that elderly suicide attempters had highrisk scores and low-rescue scores [13]. However, there has been no study conducted on the variable demographic characteristics of individuals with high-risk and low-rescue scores.

In homogeneous subgroups of DSP, we examined (1) the characteristics of high-risk suicide attempts; (2) the characteristics of low-rescue suicide attempts; and (3) the characteristics of high-risk/low-rescue suicide attempts. In line with Kim et al., we expected that the elderly would be associated with high-risk and low-rescue attempts. Our results provide other factors associated with highlethality attempts.

Methods

We retrospectively reviewed the medical records of patients who visited the emergency department (ED) of a tertiary teaching hospital in Seoul, Korea, after a drug overdose between January 1, 2000 and December 31, 2011. We used a researcher-created record form that was based on a toxicology manual and has been in use since 1997. Data from patients who overdosed on drugs but denied suicidal intent were excluded. Additionally, patients with incomplete data were excluded. Two emergency physicians independently reviewed the medical services records and medical records, including the psychiatric records. Any discrepancies were arbitrated by a third investigator. This study was approved by the Institutional Review Board of the Catholic University of Korea, Seoul Saint Mary's Hospital.

Demographic information and clinical variables such as gender, age, occupation, alcohol co-ingestion with DSP, living with a family member, numbers of previous suicide attempts, and underlying psychotic disorders were evaluated.

The RRRS was used to assess the suicide attempt's lethality. The psychometric properties of this scale have been previously validated [12]. This scale consists of ten items: five items describe risk factors (e.g., the method used, impairment of consciousness, toxicity, reversibility and treatment required), and five describe rescue factors (e.g., location, person initiating rescue, probability of discovery, accessibility of rescue, and delay until discovery) [12]. Each of the ten items was scored as 1, 2 or 3. These variables and their definitions are listed in Table 1. According to definition of the ' Agents used' factor, all DSP patients were given scores of 1 ( Table 1).

The total risk (or rescue) points are calculated by summing the score of each item to achieve a total score of 5-15 points. The total risk (or rescue) points are then converted to an overall risk (or rescue) score ranging from 1 to 5. Each of the five scores match the levels of risk (or rescue) according to the following scale: high risk (risk score 5, 13-15 risk points), high moderate (risk score 4, 11-12 risk points), moderate (risk score 3, 9-10 risk points), low moderate (risk score 2, 7-8 risk points), low risk (risk score 1, 5-6 risk points), least rescuable (rescue score 1, 5-7 rescue points), low moderate (rescue score 2, 8-9 rescue points), moderate (rescue score 3, 10-11 rescue points), high moderate (rescue score 4, 12-13 rescue points), and most rescuable (rescue score 5, 14-15 rescue points) [12]. Higher risk scores indicate more lethal suicide attempts, and higher rescue scores mean less serious and more easily rescued suicide attempts. In the present study, instead of a ratio of risk score to rescue score, we dichotomized patients into the high-risk group (risk score 4, 5) and the low-risk group (risk score 1-3) and into the low-rescue group (rescue score 1-3) and the high-rescue group (rescue score 4, 5). We analyzed the characteristics of the high-risk and lowrescue groups and evaluated the factors associated with these groups. To examine the high lethality of suicides in DSP patients, we also evaluated the factors associated with the DSP patients included in high-risk/low-rescue group.

The distributions of patient baseline characteristics are presented as the means ± standard deviations or as frequencies (i.e., percentages). Categorical variables were compared between groups using the Chi squared test, and continuous variables were compared between groups using the Student's t test. All variables with a significance level p < 0.05 in a univariate analysis were included in a multivariable logistic regression model, and odds ratios (OR) and 95 % confidence intervals (CI) were estimated. In all patients, linear associations between risk scores and rescue scores were assessed using Pearson's correlation coefficients. All statistical analyses were performed using SPSS version 16.0 (SPSS, Chicago, IL, USA), and p-values less than 0.05 were considered significant.

Results

A total of 1273 patients were screened from the poisoning database. Of these, 22 patients were excluded due to denying suicidal intent, and 137 patients were excluded from the study due to incomplete data. As a result, a total of 1114 patients were enrolled in this study. Of these patients, 253 (22.8 %) were male and 861 (77.2 %) were female. The mean age of the 1114 patients was 37.17 ± 16.76 years and ranged from 13 to 94 years. The characteristics of the enrolled patients are shown in Table 2. The frequencies of each risk and rescue factors of the patients are shown in Fig. 1. In our cohort, lower risk scores and higher rescue scores were more common than higher risk scores and lower rescue scores. Pearson's correlation analysis showed that the total risk score of DSP patients was negatively associated with the total rescue score, but the degree of correlation between the two scores was moderate (r = −0.201, p < 0.001).

In the risk analysis, 156 patients were included in the high-risk group and 958 were included in the low-risk group ( Table 3). The rate of death prior to discharge was significantly higher in the high-risk group than in the low-risk group (p < 0.001); only one patient in the lowrisk group died prior to discharge. The average ages of the patients in the low-risk and high-risk groups were 35.48 ± 15.70 and 47.57 ± 19.20 years, respectively (p < 0.001). Male gender, previous suicide attempts, and unemployment were also significantly associated with high-risk suicide attempts (p < 0.001, p = 0.043, p < 0.001, respectively). Differences in living with family members, alcohol co-ingesting with DSP, and psychiatric history were not statistically significant between the two groups. In the logistic regression analyses, the high-risk group was significantly associated with male gender (OR 3.22, 95 % CI 2.21-4.69, p < 0.001), older age (OR 1.03, 95 % CI 1.02-1.04, p < 0.001), and unemployment (OR 1.77, 95 % CI 1.20-2.62, p = 0.004) (Fig. 2).

In the rescue analysis, 200 patients were included in the low-rescue group and 914 patients were included in the high-rescue group (Table 3). Although death prior to discharge was significantly higher in the low-rescue group than in the high-rescue group (p = 0.019), 19 patients (2.1 %) in the high-rescue group died before discharge. Gender, age, unemployment, and alcohol co-ingestion were significantly different between the two groups (p = 0.002, p < 0.001, p < 0.001, p = 0.027, respectively). The between-group differences in previous suicide attempts and living with family were not statistically significant. In the logistic regression analyses, the low-rescue group was significantly associated with male gender (OR 1.58, 95 % CI 1.10-2.26, p = 0.012) and unemployment (OR 1.61, 95 % CI 1.16-2.24, p = 0.004) and negatively associated with alcohol co-ingestion (OR 0.63, 95 % CI 0.44-0.91, p = 0.012) (Fig. 2).

Finally, 42 of the total number of DSP patients showed high-lethality suicide attempts ( Table 4). The multivariate

Table 1 Risk-rescue rating

Each of the ten items were scored as 1, 2 or 3 and the total risk (or rescue) score was calculated by summing the score for each item a Self-rescue automatically yields a rescue score of 5 b If there was undue delay in obtaining treatment after discovery, the final rescue score was reduced by one point logistic regression analyses showed a significant association between high-lethality suicide attempts and male gender (OR 2.70, 95 % CI 1.41-5.18, p = 0.003), older age (OR 1.02, 95 % CI 1.01-1.04, p = 0.015), and unemployment (OR 2.98, 95 % CI 1.41-6.33, p = 0.004) ( Table 5).

Discussion

This study showed that in DSP patients, male gender, older age, and unemployment status are associated with highlethality suicide attempts. Notably, alcohol co-ingestion was significantly associated with high-rescue attempts.  The finding that some factors were associated with high-lethality attempts was in line with previous research and has been consistently reported. In general, men who attempt suicide are known to use more lethal methods, such as firearms or hanging, which often result in severe life-threatening consequences [14,15]. Among DSP patients, female patients are more common but male patients show more severe poisoning [16][17][18][19]. In this study, male gender was significantly associated with both the high-risk and low-rescue groups. A number of factors may have contributed to these gender differences [20]. First, men who are depressed are more likely to have comorbid alcohol and substance abuse problems compared with women, which places men at higher risk. Second, unlike men, who are less likely to seek and accept help or treatment, women are less impulsive, more socially embedded, and more willing to seek help [20,21].

It is well known that unemployment is a risk factor for suicide, particularly in males [22]. In a study based on a sample of the population of England and Wales, unemployment was shown to be related to a doubling of the suicide rate [23]. The results of this study showed that   unemployment was significantly associated with highlethality DSP. Age-related psychosocial stressors and family or developmental issues have a direct influence on suicide risk. In our study, the patients in the high-risk and low-rescue groups were older than the patients in the low-risk and high-rescue groups, and older age was also shown to be an independent predictor of a high-lethality suicide attempts. This finding is in line with the findings of a previous study, which showed that elderly suicide attempters had higher risk scores and lower rescue scores [13].

Especially in the rescue analysis, the lower rescue group was negatively associated with alcohol co-ingestion with DSP. This finding is generally inconsistent with those findings published in the literature. It is well known that alcoholism is associated with an increased risk of suicide, and suicide mortality rates for alcoholics are approximately six times those of the general population [2,24]. Suicide attempts involving alcohol are more likely to be impulsive [25], and people who consume alcohol before attempting suicide are also more likely to use a firearm as a suicide method [26][27][28]. When we restricted inclusion to DSP patients in this study, there was no significant difference between the high-risk and low-risk groups with regard to alcohol co-ingestion. Interestingly, acute alcohol use with attempted suicide was an independent predictor of high-rescue suicide attempts among DSP patients. Under the influence of alcohol, people are more likely to attempt suicide around other people and to talk about their intent to attempt suicide; these factors are associated with high rescue scores and probably reflect the impulsive nature of suicide attempts related to alcohol use.

The results of this study should be interpreted in the context of several limitations. A major limitation of this study was the retrospective analysis. Because researchers retrospectively reviewed the medical records (including the psychiatric records) and 11 % of the DSP patients refused or could not undergo a psychiatric interview, there was an inevitable risk of bias. Second, this study did not examine the patients' suicide attempt motives, social class, marital status, substance abuse, or other medical illnesses other than basic socio-demographic characteristics. Finally, this study used a single-center design in the country of South Korea. Nevertheless, this study used data collected over a long period of time (12 years) and included many patients. A large, prospective study including other variables may support our conclusion.

Conclusions

This retrospective study demonstrated an association between high-lethality suicide attempts and DSP patients who are male, elderly, and have no occupation. However, alcohol co-ingestion before a suicide attempt was significantly associated with high-rescue attempts.

Fig. 2
2Odds ratio for each factor associated with the high-risk or low-rescue groups in cases of deliberate self-poisoning. Each plot represents the odds ratio and the 95 % confidence interval

Table 2
2Socio-demographic characteristics of study participantsFig. 1Frequencies of scores for each risk and rescue factor among all patientsVariables
Value

Male, n (%)
253 (22.7)

Age, years (mean ± SD)
37.17 ± 16.76

Previous attempts, n (%)
248 (22.3)

Unemployed, n (%)
435 (39.0)

Not living with family, n (%)
235 (21.1)

Alcohol co-ingestion, n (%)
352 (31.6)

Psychiatric history, n (%)
294 (26.4)

Table 3
3Comparison of patient socio-demographic characteristics and risk-rescue scores in cases of deliberate self-poisoningRisk factors
Rescue factors

Low-risk (n = 958) High-risk (n = 156) p
Low-rescue (n = 200) High-rescue (n = 914) p

Male, n (%)
176 (18.4)
77 (49.4)
<0.001 62 (31.0)
191 (20.9)
0.002

Age, years (mean ± SD)
35.48 ± 15.70
47.57 ± 19.20
<0.001 39.67 ± 19.70
36.63 ± 16.00
<0.001

Previous attempts, n (%)
223 (23.3)
25 (16.0)
0.043 49 (24.5)
199 (21.8)
0.401

Unemployed, n (%)
339 (35.4)
96 (61.5)
<0.001 102 (51.0)
333 (36.4)
<0.001

Not living with family, n (%)
206 (21.5)
29 (18.6)
0.408 43 (21.5)
192 (21.0)
0.877

Alcohol co-ingestion, n (%)
294 (30.7)
58 (37.2)
0.106 50 (25.0)
302 (33.0)
0.027

Psychiatric history, n (%)
262 (27.3)
32 (20.5)
0.072 64 (32.0)
230 (25.2)
0.047

Death prior to discharge, n (%) 1 (0.1)
28 (17.9)
<0.001 10 (5.0)
19 (2.1)
0.019

Table 4
4Comparison of socio-demographic characteristics and high-lethality suicide attemptsa High-lethality suicide attempts defined as attempts with both high-risk and low-rescue suicide attempts High-lethality suicide attempts (n = 42) a Non high-lethality suicide attempts (n = 1072)p

Table 5
5Multivariate logistic regression analysis for fac-
tors associated with high-lethality suicide attempts
in cases of deliberate self-poisoning

Odds ratio
95 % confidence interval
p

Male
2.70
1.41-5.18
0.003

Age
1.02
1.01-1.04
0.015

Unemployed
2.98
1.41-6.33
0.004

AcknowledgementsWe thank all of the subjects who participated in this study.Compliance with ethical guidelinesCompeting interestsThe authors declare that they have no competing interests.Authors' contributionsHJK and SHO conceived and designed the study. YMK, SHK and KNP managed the patients in the emergency department and intensive care unit. SHK and SHO assisted with the data collection and analysis. HJK prepared the article. All authors read and approved the final manuscript.Submit your next manuscript to BioMed Central and take full advantage of:
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High incidence of rainbow glare after femtosecond laser assisted-LASIK using the upgraded FS200 femtosecond laser

Yu Zhang
Yue-Guo Chen
High incidence of rainbow glare after femtosecond laser assisted-LASIK using the upgraded FS200 femtosecond laser
10.1186/s12886-018-0734-1R E S E A R C H A R T I C L E Open Access
Background: To compare the incidence of rainbow glare (RG) after femtosecond laser assisted-LASIK (FS-LASIK) using the upgraded FS200 femtosecond laser with different flap cut parameter settings. Methods: A consecutive series of 129 patients (255 eyes) who underwent FS-LASIK for correcting myopia and/or astigmatism using upgraded WaveLight FS200 femtosecond laser with the original settings was included in group A. Another consecutive series of 129 patients (255 eyes) who underwent FS-LASIK using upgraded WaveLight FS200 femtosecond laser with flap cut parameter settings changed (decreased pulse energy, spot and line separation) was included in group B. The incidence and fading time of RG, confocal microscopic image and postoperative clinical results were compared between the two groups. Results: There were no differences between the two groups in age, baseline refraction, excimer laser ablation depth, postoperative uncorrected visual acuity and refraction. The incidence rate of RG in group A (35/255, 13.73%) was significantly higher than that in group B (4/255, 1.57%) (P < 0.05). The median fading time was 3 months in group A and 1 month in group B (P > 0.05).The confocal microscopic images showed wider laser spot spacing in group A than group B. The incidence of RG was significantly correlated with age and grouping (P < 0.05). Conclusions: The upgraded FS200 femtosecond laser with original flap cut parameter settings could increase the incidence of RG. The narrower grating size and lower pulse energy could ameliorate this side effect.

Background

Femtosecond laser-assisted flap creation in LASIK surgery has rapidly become popular with increasing safety and efficacy over time [1][2][3][4]. When using femtosecond laser for flap creation, each pulse of the laser causes the generation of a small amount of microplasma at its focal point in the corneal tissue leading to formation of microscopic gas bubbles and cavitations, which then dissipate into surrounding tissue [5,6]. Compared with mechanical microkeratome for making flaps, a key benefit of femtosecond laser technology is to provide a more precision, predictable flap and less vision threatening flap complications [7,8]. However, the femtosecond laser technique, as the resulting laser pattern may act as an optical grating, could be a major drawback. Previous studies indeed reported on perceptions of rainbow glare (RG) as a mild side effect of FS-LASIK and was verified on a model eye [9][10][11].

WaveLight FS200 femtosecond laser system (Alcon Laboratories Inc., Fort Worth, TX) is a high pulse frequency (repetition rate of 200 kHz) and comparatively lower pulse energy system that can produce flaps in a shorter period of time, around 6 s. Flaps created using FS200 demonstrated both precision and reproducibility with minimal side effects [12][13][14]. Only two previous studies reported two cases of unilateral RG following FS-LASIK with the WaveLight FS-200 femtosecond laser [15,16]. To our knowledge, there is no published study on incidence rate and alleviating management of RG when using FS200 femtosecond laser. In this retrospective study, we reported a high incidence of RG after FS-LASIK using the upgraded FS200 and evaluated the alleviative effect of adjustment of flap cut parameter settings.

Methods

Subjects

This was a retrospective cohort study conducted at Peking University Third Hospital from December 2015 to May 2017. It was carried out in accordance with the tenets of the Declaration of Helsinki and approved by the Ethics Committee of Peking University Third Hospital. An informed consent was obtained from each subject.

A consecutive series of 129 patients (255 eyes) who underwent FS-LASIK for correcting myopia and/or myopic astigmatism using upgraded WaveLight FS200 femtosecond laser with original flap cut parameter settings was included in group A. Another consecutive series of 129 patients (255 eyes) who in the period immediately following group A underwent FS-LASIK using same laser platform with flap cut parameter setting changed was included in group B.

Surgical procedures

All surgical procedures were performed by the same surgeon (Yue-guo Chen). All flaps were created by the WaveLight FS200 laser. The flap/canal/hinge parameters were as followed: flap thickness, 110 μm; flap diameter, 8.5 mm to 9.0 mm; side-cut angle, 90°; hinge angle, 50°; canal width, 1.5 mm. For group A, after FS200 laser was upgraded, the energy and laser separations settings were the same to the original one: side-cut pulse energy, 0.8 μJ; bed cut pulse energy, 0.8 μJ; stromal bed cut spot separation, 8 μm; line separation, 8 μm; side cut bed separation, 5 μm; and line separation, 3 μm (Fig. 1). For group B, all settings was the same to group A except for side-cut and bed cut pulse energy, 0.6 μJ; stromal bed cut spot separation and line separation, 6 μm (Fig. 2). Every flap was superiorly hinged, with a superior gas canal. Flaps were lifted immediately after flap creation to perform ablation using the Allegretto EX500 excimer laser (Alcon Laboratories Inc., Fort Worth, TX).

Postoperative care and follow-up

The standard postoperative regimen included one drop each of 0.5% levofloxacin (Cravit, Santen, Inc.) and 0.1% fluorometholone (FML, Allergan, Inc.) QID for 2 weeks. Each patient was followed up at days 1 and 7, and months 1, 3, 6 and 12 after surgery. The follow-up examinations involved measurements of uncorrected visual acuity (UCVA), slit-lamp examination, subjective refraction, best corrected visual acuity, corneal topography (Sirius, CSO, Italy), and HRT II confocal microscope (Heidelberg Engineering, GmbH, Dossenheim, Germany). The acquired two-dimensional image by HRT II is defined by 384 × 384 pixels covering an area of 400 × 400 μm with lateral digital resolution of 1 μm/pixel and digital depth resolution of 2 μm/pixel.

Determination of rainbow glare symptom

During follow-up, the patients complained seeing a spectrum of colored bands radiating from a white-light source when viewed in a dark environment and could match the sample pictures of rainbow glare described by Krueger RR et al [9]. Then determination of the presence of RG was made.

Statistical analysis

Data were analyzed using SPSS 21.0 (SPSS Inc., Chicago, Illinois). Independent-samples t test (normal distribution), independent-samples Mann-Whitney U test (non-normal distribution), Pearson Chi-Square test and Kaplan-Meier Fig. 1 Energy, bed spot and line parameters used for group A and one example of snapshot taken right after the completion of the interface creation analysis were used to compare data between the two groups. Spearman bivariate correlation was used to analyze the correlative factors of rainbow glare. A P value < 0.05 was considered statistically significant.

Results

Visual acuity and refractive results

There were no significant differences between the two groups in age, preoperative refraction, and excimer laser ablation depth (Table 1). At postoperative 1, 3, 12month, there were no significant differences in UCVA and spherical equivalence (SE) between the two groups (P > 0.05).

Incidence of rainbow glare

Eighteen patients (35 eyes: 17 bilateral; 1 unilateral) complained RG in group A. However, only two patients (4 eyes) reported RG in group B. The difference in the incidence rate of rainbow glare between the two groups was statistically significant (P < 0.05) (Fig. 3). The symptom arose within one week among most of the patients (18/20, 90%).

Fading time of rainbow glare

Rainbow glare faded away as time went by. In group A, all the patients experienced complete resolution of symptom by the 16th postoperative month. In group B, rainbow glare faded away by the 6th postoperative month (Fig. 4). The median fading time was 3 months in group A and 1 month in group B. However, there was no significant difference in fading time between the two groups (Log Rank, χ 2 = 0.044, P = 0.834).

Confocal microscopic results

HRT II confocal microscope equipped with the Rostock corneal module showed the femtosecond laser spots, spot spacing and grating pattern clearly at approximately 1001 10 μm level below the anterior corneal surface. The images showed comparatively wider laser spot spacing in a patient of group A (Fig. 5a) and narrower spot spacing in a patient of group B (Fig. 5b). Although much more undistinguishable with time, this phenomenon could be found even at 12 months after surgery.

Comparison between eyes with RG and eyes without RG

Among all the 510 eyes, no significant differences in refraction, UCVA and ablation depth were found between eyes with RG (39 eyes) and eyes without RG (471 eyes). However, the mean age of subjects with RG was significantly older than that of subjects without RG (P < 0.05). (Table 2).

Correlative factors of rainbow glare

In all the 510 eyes, the occurrence of RG was significantly correlated with age (r = 0.115, P < 0.05) and grouping (r = 0.229, P < 0.001), but not with baseline SE, baseline cylinder or excimer laser ablation depth (P > 0.05).

Discussion

LASIK is the main stream refractive surgery procedure for correction of myopia and myopic astigmatism   [9,10]. The cause of RG was defined as the diffraction of light from the grating pattern created on the back surface of the LASIK flap after femtosecond laser exposure [17]. The first clinical study describing RG found an incidence rate of 19.07% with an older high pulse energy, low frequency (15 kHz) IntraLase femtosecond laser model [9]. A subsequent study reported an incidence of rainbow glare of 5.8% with a newer generation of lower pulse energy, higher frequency (60 kHz) IntraLase model [10]. Since low pulse energy and high frequency femtosecond laser system was applied, less incidence of RG has Fig. 3 Incidence rate of rainbow glare in the two groups Fig. 4 The fading time of rainbow glare in the two groups been reported. The first case of RG with WaveLight FS200 femtosecond laser platform, the lower pulse energy and higher frequency system, was reported by Gatinel D, et al. in 2013 [15]. We have used the Wave-Light FS200 femtosecond laser platform (≤0.8 μJ pulse energy for flap, 200 kHz frequency) for performing FS-LASIK since 2011 with the recommended optimized cut settings of 0.80 μJ pulse energy, 8.00 μm spot and line separations for stromal bed cut and barely heard complaining of the RG symptom until the laser platform was upgraded to "Green" version in December, 2015. As we understood, "Green" brought only new features of software, such as patient data management, docking guidance system and flap position registration on the basis of pupil center for more convenient without changing any laser parameter settings. After the high incidence of RG was noted, we got a new recommendation of optimized cut settings of 0.60 μJ pulse energy, 6.00 μm spot and line separations, the tolerable arrangement of former version, for stromal bed cut. Since then, the incidence rate of RG had decreased to 1.57% among the following consecutive series cases.

Up to date, the reason why the incidence of RG significantly increased after machine being upgraded has still been unclear. The quality of the focused laser beam and numerical aperture of the focusing optics appears to be the most significant factor in minimizing the diffractive dispersion of light that leads to RG [9,10]. Otherwise, the relationship between length of time between service calls and the incidence of RG emphasized the importance of a proper maintenance and alignment of the optical components used to focus the beam [10].

Gatinel D, et al. calculated a grating pattern spacing of 7.90μm was the cause of rainbow glare [15]. This value was very close to 8.0μm in group A of the present study. It might seem that reducing the spot/line separation could increase the angle of the spectral pattern sufficient enough to avoid photodetection by the retina altogether [17]. Correlative results in the present study showed that less laser pulse energy and narrower spot/line separations might play an important role in decreasing the incidence of RG. With the settings changed, the energy intensity and the flap cutting time (from 6 to 7 s to 12-13 s) increased, which may induce more corneal lamellar tissue reaction and should be observed for longer time.

With intensively in vivo confocal microscope scan, at around the 110 μm depth level from the anterior corneal surface of almost all the cases after FS-LASIK in this study, no matter whether RG existed or not, the hyperreflective spots could be found in an equidistant gridlike pattern with HRT II confocal microscope. The spot pattern matched the laser spots/lines separation of  8.0μm or 6.0μm programmed in the FS200 femtosecond laser. There was a report of successful RG correction using undersurface ablation of the LASIK flap [16]. We considered that it was not necessary to do so if merely the symptom of RG existed, because the symptom of RG disappeared with time in all the subjects of the present study, even when the hyper-reflective spots were still visible under confocal microscopy. In this aspect, the patients' individual variation of sensitivity and selfadaption might play a pivotal role. Krueger et al. found an increased degree of myopic correction was associated with an increased incidence of RG [9]. However, the present study did not found correlation between initial SE or ablation depth and the incidence of RG, which was consistent with the study of Bamba et al [10]. The present study first found that older age was a correlative factor of RG. Only one previous report studied the relationship between age and the incidence of RG and found negative result [10]. Different race of subjects and femtosecond laser machine might lead to the reverse results. RG was subjective feeling, and older subjects might have more need in night vision, such as driving in the night, which might cause more older subjects felt and reported RG to the doctor. More prospective large-sample studies are needed in future to explore the risk or correlative factors of RG.

Conclusions

In conclusion, the upgrade of WaveLight FS200 femtosecond laser with original flap cut parameter settings could increase the incidence of RG. After the pulse energy, spot/line separations for stromal bed cut was adjusted, the incidence of RG decreased significantly. RG did not influence postoperative visual acuity or refractive results. It faded away as time went by. Narrower grating size and lower pulse energy could significantly ameliorate this side effect after FS-LASIK when using WaveLight FS200 femtosecond laser.

Abbreviations FS-LASIK: femtosecond laser assisted-laser in situ keratomileusis; RG: rainbow glare; SE: spherical equivalence; UCVA: uncorrected visual acuity

Fig. 2
2Energy, bed spot and line parameters used for group B and one example of snapshot taken right after the completion of the interface creation

Fig. 5
5Three months post-operation, HRT II corneal confocal microscopy showed comparatively wider laser spot spacing in a group A patient (a) and narrower spot spacing in a group B patient (b) (original magnification 400 × 400 μm)

Table 1
1Refraction and demographic data of the two groups (x±s/ M) SE spherical equivalent, Cyl cylinder, AD excimer laser ablation depth worldwide. The introduction of the femtosecond laser in LASIK enabled the creation of lamellar corneal flaps in a more accurate, more stable, evener and safer manner. Even though RG which was first reported in 2008 by Krueger et al. is a mild and temporary optical side effect of FS-LASIK [9], it may disturb patients' night driving and decrease their satisfactionGroup A
Group B
t / Z
P

Age (years)
27.62±6.25
26.24±7.02
1.597
0.111

SE(D)
−6.36±2.04
−6.47±2.26
0.617
0.535

Cyl(D)
−0.75 (0~− 3.25)
−0.75 (0~− 4.25)
2.143
0.062

AD (μm)
92.06±21.02
94.71±22.20
−1.43
0.153

Table 2
2Comparison between eyes with rainbow glare and eyes without rainbow glare (x±s / M) SE spherical equivalent, Cyl cylinder, AD excimer laser ablation depth, RG rainbow glare, 1 M UCVA uncorrected visual acuity at postoperative 1 monthWith RG
(39 eyes)

Without RG
(471 eyes)

t / Z
P

Age (years)
29.97±5.01
26.75±6.76
2.584
0.013

Baseline SE (D)
−6.34±2.13
− 6.42±2.16
0.232
0.816

Baseline Cyl (D)
−0.75 (0~− 3.50)
−0.75 (0~− 4.25)
0.113
0.984

AD (μm)
90.59±22.96
93.57±21.53
−0.827
0.409

1 M UCVA(LogMAR)
−0.04(− 0.15~0.15)
−0.04(− 0.18~0.22)
0.206
0.955

1 M SE (D)
0.00 (0.00~1.00)
0.13 (− 1.00~1.00)
0.198
0.961

AcknowledgementsNot applicable.FundingThe authors declare that they have no funding.Availability of data and materialsThe datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.Authors' contributions YZ analyzed and interpreted the patient data, and wrote the manuscript. YC designed the study, collected data and revised the manuscript. All authors read and approved the final manuscript.Ethics approval and consent to participateThis study was carried out in accordance with the tenets of the Declaration of Helsinki. It was approved by the Ethics Committee of Peking University Third Hospital. An informed consent was obtained from each subject.Consent for publication Not applicableCompeting interestsThe authors declare that they have no competing interests.Publisher's NoteSpringer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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