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Clinical Studies and Practice

Circadian rhythmicity as a predictor of weight-loss effectiveness



Some of the major challenges associated with successful dietary weight management include the identification of individuals not responsive to specific interventions. The aim was to investigate the potential relationship between weight loss and circadian rhythmicity, using wrist temperature and actimetry measurements, in women undergoing a weight-loss program, in order to assess whether circadian rhythmicity could be a marker of weight-loss effectiveness.


Participants were 85 overweight and obese women (body mass index, BMI: 30.24±4.95 kg m−2) subjected to a weight-reduction program. Efficacy of the treatment was defined as total weight loss, percentage of initial weight and weekly weight loss rates. Circadian rhythmicity in wrist temperature motor activity and position were analyzed using different sensors.


Lower weight loss was related with a more flattened pattern measured as amplitude from cosinor (r=0.235, P=0.032), a higher fragmentation of rhythms determined by higher intradaily variability (IV) (r=−0.339, P=0.002), and an impaired wrist temperature circadian rhythm determined by the means of Circadian Function Index (r=0.228, P=0.038). Further analyses showed that low responders displayed lower amplitude (0.71±0.36 versus 1.24±0.62, P=0.036) and higher fragmentation of the circadian rhythm (0.24±0.11 versus 0.15±0.07, P=0.043) than high responders. Whereas we did not find significant differences in total activity rates between high responders and low responders, we found significant differences for the mean values of body position for high responders (39.12±3.79°) as compared with low responder women (35.31±2.53°, P=0.01).


Circadian rhythms at the beginning of the treatment are good predictors of future weight loss. Further treatment should consider chronobiological aspects to diagnose obesity and effectiveness of treatments.


Some of the major challenges associated with successful dietary weight management include the identification of those individuals who are not responsive to specific interventions, as well as the ability to maintain the potential short benefits into the long term.1, 2, 3, 4 Analysis of the interaction between genes, dietary and behavioral factors has provided so far some tantalizing suggestions about the possibility to use this approach as a tool for more successful personalized dietary interventions.5 Along these lines, we have reported that common genetic variations within clock genes (that is, CLOCK 3111T/C) are associated with decreased response to weight-loss interventions.6 The differences in circadian rhythmicity that exist between both genetic variants (C or T) may be accounting for the differences in weight-loss effectiveness.7

Furthermore, current evidence suggests that the timing of the meals can influence metabolic syndrome by affecting circadian rhythms. This evidence has fostered increasing interest on the notion that metabolic syndrome and obesity are associated with chronodysruption.8, 9 This is also supported by the fact that obese individuals appear to be less responsive to environmental cues and diurnal variations in body temperature are diminished in obese humans.10 On the other hand, it has been proposed that an alteration in the body-temperature pattern early in life may predict further obesity in dogs.11

Therefore, it can be hypothesized that the circadian system could be implicated in the effectiveness of weight-loss interventions.7 Indeed, the major components of energy homeostasis, including the sleep–wake cycle, thermogenesis, feeding, and glucose and lipid metabolism, are subjected to circadian regulation that synchronizes energy intake and expenditure with changes in the external environment driven by the Earth’s rotation.12

One of the most practical and informative measures used to evaluate and diagnose circadian disorders is skin temperature. This has been evaluated for different ages13, 14, 15, 16 and pathologies.17 Recently, it has been published that peripheral body temperature measured at the wrist (herein after called wrist temperature) rhythm exhibits a strong endogenous component, despite the existence of multiple external influences, and it has been proposed that it could be considered a valuable and minimally invasive measure to assess circadian physiology under ambulatory conditions.18 Moreover, a recent study has demonstrated that wrist-temperature phase indexes are capable of accurately detecting the phase of the circadian system in subjects recorded under normal living conditions as compare with Dim Light Melatonin onset.19 Rest–activity- and body-position rhythm measurements using actimetry are also simple, non-invasive methods for indirectly evaluating the sleep–wake cycle.

Therefore, our main objective was to investigate the potential relationship between total weight loss and circadian rhythmicity, using wrist temperature and actimetry measurements, in women undergoing a behavioral therapy treatment based on Mediterranean diet, in order to assess whether circadian rhythmicity could be a marker of weight-loss effectiveness.

Materials and methods

Study population

We recruited 85 overweight and obese women with the mean body mass index (BMI) 30.24±4.95 kg m−2 and age 39.84±12 years. All subjects attended outpatient obesity clinics in the city of Murcia located in southeastern Spain. Exclusion criteria included the diagnosis of insomnia, cognitive disorders, diabetes mellitus, chronic renal failure, hepatic diseases or cancer. Moreover, women taking thermogenic, lipogenic, sleep drugs or melatonin were also excluded from the study. All procedures were in accordance with good clinical practices and were approved by the Ethical Committee of the University of Murcia (Murcia, Spain).


The weight-loss intervention program has been described elsewhere in detail.20 In brief, participants attended weekly 60-min group therapy sessions during an average of 5 months. The duration of the program was variable and it depended on the individual weight-loss goal. After reaching their pre-established weight-loss goals, they were placed on a 5-month maintenance program. During this period, meetings were held first every 2 weeks and then monthly. Throughout the entire program, sessions were conducted by a nutritionist. The intervention program was based on the following four components: (1) dietary advice, based on the principles of the Mediterranean diet, in which the distribution of macronutrient followed the recommendations of the Spanish Society of Community Nutrition;21 (2) nutritional education; (3) moderate physical activity; and (4) cognitive-behavioral modification, including stimulus control, self-monitoring, positive reinforcement, preventing relapse and cognitive restructuring.22 To evaluate effectiveness of the program, the following variables were evaluated: percentage of weight loss (%), total weight loss (kg) and the rate of weight loss (g per week). The measures used for this purpose are described below.

Obesity-related parameters

Body height was measured to the nearest 0.1 cm using a stadiometer (Harpenden digital stadiometer (rank, 0.7–2.05)) with subjects barefoot in the free-standing position. Body weight was measured with subjects wearing light clothing with no shoes to the nearest 0.05 kg, using calibrated body scale. Height and weight measurements were always obtained at the same time of day. BMI was calculated as weight (kg) per height (m2). Total body fat was measured by bioelectrical impedance, using the TANITA TBF-300 (Tanita Corporation of America, Arlington Heights, IL, USA) equipment. Subjects were requested not to drink liquids during the 2-h period prior to the measurements.

Metabolic syndrome features

Body fat distribution was estimated by measuring waist circumference, midway between the lower rib margin and the iliac crest, and hip circumference, the widest girth over the great trochanters.

Biochemical analyses were performed on plasma and serum samples obtained from venous blood attained by venipuncture after an overnight fast. Samples were stored at −80 °C and analyzed in a single batch. Serum glucose concentration was measured in duplicate using the glucose oxidase method. Plasma concentrations of triacylglycerols, total cholesterol, high-density lipoprotein and low-density lipoprotein cholesterols were determined with commercial kits (Roche Diagnostics GmbH, Mannheim, Germany). Blood pressure was measured every 2 days during a 1-week period. Measurements were carried out consistently before lunch (1400 hours) and before dinner (2100 hours), using an Omron RX3 wrist blood pressure monitor (OMRON Electronics Iberia SA, Madrid, Spain).

Assessment of the circadian system status

Wrist temperature rhythm

Wrist temperature rhythm was assessed continuously for 8 days using a temperature sensor (Thermochron iButton DS1921H; Dallas, Maxim, Dallas, TX, USA) with a sensitivity of 0.125 °C and was programmed to sample every 10 min. Measurements were carried out under basal conditions before getting involved in the weight-loss program. It was attached to a double-sided cotton sport wrist band, and the sensor surface was placed over the inside of the wrist on the radial artery of the non-dominant hand, as described previously by Sarabia et al.12 The information stored in the iButton was transferred through an adapter (DS1402D-DR8; Dallas, Maxim) to a personal computer using iButton Viewer v. 3.22 (Dallas Semiconductor MAXIM software provided by the manufacturer). Data were recorded during the months of November to May, with environmental temperatures ranging between 16.1 and 21.3 °C (data obtained from the Center for Statistics of Murcia), to minimize the influence of extreme environmental temperatures on wrist temperature.

Body position and rest–activity rhythm

The body position and rest–activity rhythm were assessed over the same 8 days using a HOBO Pendant G Acceleration Data Logger UA-004-64 (Onset Computer, Bourne, MA, USA) placed on the non-dominant arm by means of a sports band, with its x axis parallel to the humerus bone. The sensor was programmed to record data every 30 s.

The information stored in the actimeter was transferred through an optical USB Base Station (MAN-BASE-U-4, HOBO; Onset Computer) to a personal computer using the software provided by the manufacturer (HOBOware 2.2). From the information provided by the actimeter, we defined the following two variables: motor activity (A) and body position (P). First, A was calculated as degrees of change in x, y and z axis positions with respect to the previous sampling time as described by Ortiz-Tudela et al.23 Second, P was calculated as the angle between the x axis of the actimeter and the horizontal plane, with its value being 0° when the arm is in a horizontal position and 90° when it is vertically aligned.23

Statistical methods and variables obtained

To characterize wrist temperature, activity and position rhythms, we calculated the following parameters using parametric and non-parametric methods.

Cosinor’s analysis

Mesor: Mean value of the rhythm fitted to a cosine function.

Amplitude: Difference between the maximum (or minimum) value of the cosine function and mesor.

Acrophase: Timing of the maximum value of the cosine function.

Fourier analysis

First harmonic’s power (P1): Spectral power of the 24-h rhythm.

Second harmonic’s power (P2): Amplitude of the 12-h rhythm.

Twelfth harmonic’s accumulated power or circadian index (PACUM12): Indicates the accumulated spectral power of the first twelfth harmonics (from 24- to 2-h periods).

Non-parametric analysis

Interdaily stability (IS):24 The similarity of the 24-h pattern over days. It varied between 0 for Gaussian noise and 1 for perfect stability, where the pattern repeated itself exactly day after day.

Intradaily variability (IV):24 This characterizes the rhythm fragmentation. Its values oscillated between 0, when the wave was perfectly sinusoidal, and 2, when the wave was as Gaussian noise.

The average 10 consecutive hours of maximum values and its timing were calculated for activity and position patterns.

Relative amplitude:24 It was calculated by the difference between M5 and L10 divided by the sum of M5 and L10.

Circadian function index (CFI):24 In order to classify individuals according to their circadian system functionality, we used a new scoring index—the CFI)—which was previously proposed by our laboratory.23 This index was calculated by averaging three nonparametric indices (IS, IV and relative amplitude) for temperature and sleep data. Before averaging, all these indices were normalized between 0 and 1, (IV was inverted, as its values are opposite to those for IS and relative amplitude). Accordingly, the CFI oscillates between 0 (gaussian noise) and 1 (a sinusoidal wave).23 All these rhythmic parameters were obtained by using an integrated package for temporal series analysis ‘Circadianware’ (Chronobiology Laboratory, University of Murcia, 2010).

Other analyses

A two-way analysis of variance for WT with effectiveness of the treatment and time as variables was used to identify any differences between high and low responders in wrist-temperature evolution over time.

The mean values of wrist temperature, actimetry and body position variables were compared between high and low responders, by analysis of covariance, after adjustment for BMI and age.

Statistical analyses were performed using the SPSS 15.0 software (SPSS, Chicago, IL, USA). A two-tailed P-value of <0.05 was considered statistically significant.


Participating women achieved an average weight loss of 8.5 kg (10% of initial weight) during the first 21 weeks on the Program, which translated into an average weight loss rate of 460 g per week. However, we observed a dramatic inter-individual variability in response (Figure 1). Whereas most women lost between 5 and 10% of their initial body weight, high responders (upper decile) lost 15–30%, whereas low responders (lowest decile) gained (1%) weight during the intervention. Table 1 shows the baseline characteristics of the women studied according to responsiveness (high responder- and low responder status).

Figure 1

Responsiveness to the treatment.

Table 1 General characteristics of the women studied (n=85) according to responsiveness status

We then investigated whether this variability in response was related to circadian rhythmicity. For this purpose, we analyzed the individual responses in relation with temperature, actimetry and body-position rhythms. We found significant correlations between wrist-temperature rhythmicity variables, such as amplitude and fragmentation (IV) of the rhythm, and the efficacy of the intervention (Table 2). The mean wrist temperature was inversely correlated with response to the intervention. Thus, the lower mean wrist temperature was associated with higher weight loss (%). Conversely, the CFI was positively correlated with the speed to lose weight (weight-loss rate) (Table 2).

Table 2 Significant correlations between the wrist temperature rhythmicity variables and weight-loss effectiveness

When we represented the mean values of wrist temperature, actimetry and body position, according to the responsiveness status, across the 24-h period (Figure 2), we found statistically significant differences along the day in circadian rhythmicity patterns between high responders and low responders for wrist temperature and body position (analysis of variance, P<0.05); however, no significance was achieved for actimetry.

Figure 2

Daily mean waveform of wrist temperature (a), actimetry (b), position (c), recorded over 8-day period. LR (low responders) and HR (high responders). Significant differences were found between both curves from 0700 to 2300 hour in wrist temperature and position (analysis of variance, P<0.05).

The temperature variation followed the expected profile. It rose just before sleep time, remained elevated overnight and decreased after waking up (Figure 2a). We noted a second peak in the afternoon, and during the ‘wake maintenance zone’ (period occurring between 1900 and 2200 hours) the temperature achieved the lowest values of the day.

Low responder women had significantly higher daytime L10 and mesor values (33.17±0.69 and 33.72±0.62 °C) as compared with high responder women (32.19±0.89 and 33.07±0.72 °C; P=0.01 and P=0.047, respectively).

Moreover, we found statistically significant differences in wrist temperature for the relative amplitude and the rhythm fragmentation (IV) between high responders and low responders, but not so for stability (IS) (Figure 3), suggesting that differences in wrist-temperature variables were not related to phase instability.

Figure 3

Differences in temperature amplitude (a), intradaily variability (b) and IS (c) between higher and lower responders (HR and LR). AU: arbitrary units.

Fourier analysis showed differences between high responders and low responders in wrist temperature P1 (24-h peak) (°C) (0.52±0.36 versus 1.94±2.07 °C, P=0.017), P2 (12-h peaks) (0.33±0.17 versus 0.63±0.60 °C, P=0.001) and the Pacum12 (1.23±0.57 versus 3.10±2.60 °C, P=0.027). The predominance of the 24-h peaks among high responders and the decrease in the ultradian peaks (both 12 h and Pacum12 as compared with the low responders) suggest that patterns of wrist temperature of high responders were better adjusted to a cosine function.

Daily activity and position patterns (Figures 2b and c, respectively) showed lower values and lower variability during the night time. Furthermore, activity and position had lower values during the postprandial period. We did not find significant differences in total activity rates (metabolic units) between high responder- and low responder women (3189±4972 versus 4356±4098, P=0.574); however, we found significant differences for the mean values of body position for high responder- (39.12±3.79°) as compared with low responder women (35.31±2.53°, P=0.01).


Our results provide the foundation to propose that circadian rhythmicity may be a significant and useful predictive factor of weight-loss effectiveness. Specifically, we demonstrate that the high variability in weight-loss response found in these women was significantly related to the circadian rhythmicity of wrist temperature. Thus, the high amplitude of the rhythm and the low fragmentation were both predictors of higher-weight loss and efficacy of the intervention. Overall, these findings are in agreement with our initial hypothesis that high and low responders to a weight-loss intervention would differ in the diurnal pattern of peripheral body temperature and position, as reflected by, both, differences in the mean values and in circadian responsiveness.

In this study, high responders had more robust circadian rhythm profiles than low responders, characterized by some relevant circadian abnormalities such as lower amplitude and greater fragmentation of the rhythm, and a significantly weakened circadian function, as assessed by the CFI. Moreover, we found significant differences between high responders and low responders for the mean temperature values, the later having increased values, resulting from skin vasodilatation probably due to parasympathetic activation, which has been related with sleepiness and high relaxed states.25

Interestingly, among the differences in circadian parameters, the reduced amplitude found in low responders is one of the hallmarks of aging. An attenuated amplitude has been observed in elderly subjects as well as in the obese26 and in Alzheimer’s patients.24, 27 A healthy wrist-temperature pattern that characterizes ideal body-weight individuals shows wide amplitude with high-temperature values during sleep time and a pronounced drop after arising in the morning with low values during wake time. There is also a secondary peak around afternoon, a period associated with naps, after their usual lunch time, and a dip between 2000 and 2200 hours, a period already known as the ‘wake maintenance zone’.17 In general, a ‘healthy circadian pattern’ includes the following: high amplitude, high regularity (high IS) and low fragmentation (low IV).17

In the current work, wrist-temperature IV was also related to the effectiveness of the intervention, suggesting a greater fragmentation of the rhythm in low responders as compared with high responders. IV is a measure of fragmentation of the rhythm that seems to be dependent on endogenous circadian disturbances, as it shows a low correlation with functional, social and emotional well being.28 Several studies have shown that fragmentation increases with illnesses in association with dementia, cognitive decline, aging, obesity and so on.29

Moreover, our group has previously demonstrated that the increase in the rhythm fragmentation with obesity is related to a decrease in melatonin amplitude, which has been described as a biological sign of chronodisruption.26

It is important to highlight the fact that in the current work, significant differences were found in IV between high responders and low responders, whereas no significant differences were found in the IS, which suggests that these differences in wrist-temperature variables were not because of phase instability.

Among the circadian variables analyzed, the CFI is a suitable and accurate index to define subjects’ circadian status. This parameter integrates three complementary circadian parameters—IV, relative amplitude and IS—allowing us to classify the circadian system status of the participants.23 In the current study, the CFI was significantly higher in high responders than in low responders, supporting the notion that a healthy circadian pattern is related to, and may be a marker of, better weight-loss effectiveness in obesity treatment. Interestingly, the CFI seems to be a good predictor of the speed to lose weight (weight-loss rate), whereas, for example, the mean wrist temperature or amplitude seems to be a good predictor of weight-loss percentage. On the other hand, IV is a good predictor for both situations (total weight loss and speed).

Finally, it is important to highlight that the patterns of wrist temperature in high responders were better adjusted to a cosine function than those of low responders. Moreover, the pronounced drop of temperature after arising in the morning that characterized the circadian pattern of high responders was attenuated in low responders. Another important difference was related to the ultradian rhythms (from 12- to 2-h periods) and to the CFI. Low responder women were characterized by a decrease in the second harmonic and with a diminished circadian index. We have previously shown that the absence of this second circadian harmonic peak (P2) is a marker of chronodisruption and metabolic alteration and is also related to obesity.26

A number of factors may be behind these differences in circadian rhythmicity between high responder and low responder women. Obesity itself may alter the circadian system. An obese state may actually exacerbate circadian imbalances in humans. Along these lines, individuals suffering from obesity and diabetes have the amplitudes of their rhythms dampened.26,30 However, this is not the case in the current study taking into account that the differences in circadian rhythmicity between high responders and low responders were significant even after adjusting for obesity; more importantly, high responders were the women who displayed healthier circadian rhythms in spite of being significantly more obese than low responders. Differences in energy expenditure could be responsible for these results. Indeed, whereas no significant differences in total activity rates and physical activity circadian patterns were found between high responders and low responders, we did find significant differences in position patterns, with higher position values during daytime among high responder women. Moreover, the timing of meals,31 circadian gene variants7, 32 or abnormal eating behavior33 could be also driving the results. Regardless of the influencing factors, the current study supports the concept that circadian rhythmicity is implicated in weight-loss effectiveness and it could be considered as a predictor factor of weight-loss effectiveness.

In summary, in our study, low responder women displayed a less marked circadian rhythm than high responders. Characterizing the circadian pattern of wrist temperature, activity and position in ambulatory conditions may guide health professionals to predict the success of weight-loss programs and to implement behavioral advice about external synchronizers such as timing of food intake, physical activity and sleep that will contribute to achieve a healthier circadian system and subsequently more effective weight loss.


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We want to thank all the patients for their participation in this study, and also José Cánovas Lorente for his support and help in data collection and processing.

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Correspondence to M Garaulet.

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The authors declare no conflict of interest.

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Bandín, C., Martinez-Nicolas, A., Ordovás, J. et al. Circadian rhythmicity as a predictor of weight-loss effectiveness. Int J Obes 38, 1083–1088 (2014).

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  • circadian rhythm
  • weight loss
  • treatment efficacy
  • low and high responders

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