# Effect of a home-based videoconferencing exercise training program on circadian rhythms and sleep quality in healthy older adults.

Emma Milot, A. Langeard, S. Rehel, L. Bigot, Antoine Gauthier, N. Bessot, G. Quarck

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Emma Milot, A. Langeard, S. Rehel, L. Bigot, Antoine Gauthier, et al.. Effect of a home-based videoconferencing exercise training program on circadian rhythms and sleep quality in healthy older adults.. Sleep Medicine, inPress, 10.1016/j.sleep.2025.106746 . hal-05216645

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Effect of a home-based videoconferencing exercise training program on circadian rhythms and sleep quality in healthy older adults.

Milot E., Langeard A., Rehel S., Bigot L., Gauthier A., Bessot N., Quarck G.

PII: S1389-9457(25)00421-6

DOI: https://doi.org/10.1016/j.sleep.2025.106746

Reference: SLEEP 106746

To appear in: *Sleep Medicine*

Received Date: 23 January 2025

Revised Date: 28 May 2025

Accepted Date: 13 August 2025

Please cite this article as: Milot E, Langeard A, Rehel S, Bigot L, Gauthier A, Bessot N, Quarck G, Effect of a home-based videoconferencing exercise training program on circadian rhythms and sleep quality in healthy older adults., *Sleep Medicine*, https://doi.org/10.1016/j.sleep.2025.106746.

This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

© 2025 Published by Elsevier B.V.

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# Effect of a home-based videoconferencing exercise training program on circadian rhythms and sleep quality in healthy older adults

Milot et al., 2025

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43 healthy older
adults aged 61 to
79 years old

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## Exercise training (ET) group (n=21)

12-16 weeks of home-based aerobic and strength training

Via videoconferencing
3 x 60 minutes per week

<figure><img src="image_6.png" /></figure>

## Active control (AC) group (n=22)

12-16 weeks of home-based health education courses

Via videoconferencing
1 x 60 minutes per week

<figure><img src="image_7.png" /></figure>

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* p<.05; ** p<.01; *** p<.001

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Pre-

<figure><img src="image_13.png" /></figure>

Post- interventions

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Actigraphy

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Ambulatory
polysomnography

<figure><img src="image_16.png" /></figure>

Sleep
questionnaires

## Conclusions

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### improved:

* ✓ Rest-activity rhythms

* ✓ Sleep efficiency

* ✓ Subjective sleep quality perception

* ✓ Feasible and beneficial for healthy older adults
1 **TITLE PAGE**

2 **Title:** Effect of a home-based videoconferencing exercise training program on circadian

3 rhythms and sleep quality in healthy older adults.

4 **Authors:** Milot E.¹, Langeard A.¹, Rehel S.¹, Bigot L. ¹,², Gauthier A.¹, Bessot N.¹† & Quarck G. 1†

5 **Affiliations:**

6 (1) University of Caen Normandy, INSERM, COMETE U1075, CYCERON, CHU of Caen, France

7 (2) MOOVEN, France

8 (†) These authors share last authorship

9 **Corresponding author's name:** Emma Milot – emma.milot@unicaen.fr

10 **Abstract:** The aging process affects circadian rhythms and sleep quality, leading to various disruptions and health challenges. These changes, along with increased sedentary behavior, contribute to deteriorating sleep quality and overall health. Physical activity is crucial for entraining circadian rhythm and sleep improvement. Despite the well-explored benefits of face-to-face physical training, accessibility barriers for older adults necessitate innovative approaches like videoconferencing interventions. This study evaluated the effects of a home-based videoconferencing exercise training (ET) program on circadian rhythms and sleep quality in healthy older adults. Forty-three participants (61–79 years) were randomized to either remote physical ET or health education sessions (active control (AC) group). Pre- and post-intervention evaluations included circadian rhythm assessment using 7-day actigraphy and sleep quality via two polysomnography sessions. Significant differences between groups were observed in Z-score changes for mesor and amplitude values. Significant differences between groups in Z-score changes were also observed for sleep efficiency, the percentage of stage 1 sleep values, and insomnia severity index scores between. The study findings suggest that a home-based videoconferencing ET program positively impacts circadian rhythms and sleep quality in healthy older adults. This non-pharmaceutical approach offers an accessible and scalable solution to address age-related sleep disturbances and enhance overall well-being. However, findings on objective and subjective sleep parameters are preliminary and need confirmation in larger, standardized studies.

29 **Keywords:** Biological rhythms, Sleep, Polysomnography, Actigraphy, Aging, Exercise, Web-based interventions, Well-being.

31 **Highlights**

32 • Home-based videoconferencing exercise improves circadian rhythms and sleep quality in older adults

34 • First trial to evaluate videoconferencing exercise effects on rest-activity rhythm and objective and subjective sleep quality in older adults
36

37

* Accessible, non-pharmaceutical solution effectively targets age-related sleep disturbances
38 **INTRODUCTION**

Poor sleep quality poses a significant challenge for older adults, with the prevalence of sleep problems reaching 50% in this population [1]. Impaired sleep quality negatively impacts both mental and physical health [2]. Such disturbances have been identified as a significant determinant of diminishing overall satisfaction with quality of life [3], while concurrently amplifying the risk of developing cardiometabolic conditions and premature mortality [4]. Older adults commonly experience challenges in initiating and maintaining sleep, prolonged periods of wakefulness during the night, reduced duration of slow-wave-sleep, increased feelings of fatigue, and excessive daytime sleepiness [5,6].

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The circadian rhythms are the most prominent biological rhythms and comprise different
rhythms such as alertness, body temperature, hormone production (melatonin for example),
but also the sleep wake cycle. The aging process affects circadian rhythms and sleep patterns
[7]. This deterioration in circadian rhythms is likely due to various alterations within the
circadian timing system. These alterations include disruptions in the central biological clock
[8], modifications in the relay mechanisms of these clocks, such as changes in melatonin
regulation [9], and a diminished influence of external synchronizers on the body clock [10].
Research supports a consensus regarding age-related changes in key components like the
suprachiasmatic nuclei (SCN) and hypocretin/orexin neurons [11]. These alterations result in
a diminished rhythm of multi-unit activity [12], disruptions in clock gene expression [13], and
an imbalance between sleep and wakefulness [14]. Furthermore, circadian rhythms such as
activity-rest cycle, temperature, or melatonin secretion in older adults often manifest with
decreased amplitudes and a phase advance [15–17]. This decline is primarily attributed to
reduced day-to-night variations of circadian rhythms [16,17], heightened frequency of
napping [18], and inadequate exposure to light [19]. Aging is often linked to increased
sedentary behavior [20], which disrupts circadian rhythms by blurring the distinction between
periods of activity and rest, leading to reduced wakefulness and sleep quality [16].
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Exercise significantly enhances physical and mental well-being in older adults and has an
immediate positive effect on circadian rhythms by improving daily temperature amplitude in
healthy adults [21–23]. However, studies on chronic exercise's impact on circadian rhythms in
older adults are limited. Despite the gap, the evidence does support the benefits of regular
exercise on sleep in this population [24,25]. A recent review of 14 exercise trials found that
only 3 used objective sleep measures like polysomnography or actigraphy, while most relied
on self-reported questionnaires [24]. This highlights a gap in literature. Our upcoming studies
aim to address this gap and further explore how exercise affects sleep quality and circadian
rhythms in older adults.
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Regular exercise training (ET) offers many benefits, but face-to-face sessions can be
challenging for older adults due to cost and distance [26]. In-home ET via videoconferencing
has been shown to improve overall health in this population [27,28], yet its impact on activity-
76 rest cycles and sleep has not been extensively studied. While videoconferencing provides a
77 practical alternative, it may be less effective than in-person training for certain physical
78 functions [29]. This raises questions about whether the benefits of exercise on circadian
79 rhythms and sleep quality are the same with videoconferencing.

80 The main objective of this study is to evaluate the effect of a home-based videoconferencing
81 ET program on circadian rhythms and sleep quality among healthy older adults compared to
82 the active control (AC) group. We hypothesize that participation in the ET will result in
83 increased robustness of circadian rhythms and improved sleep quality compared to an AC
84 group (health education).

85 MATERIALS AND METHODS

86 Study design

87 Two studies, Study 1 (2017) and Study 2 (2022), utilized data from randomized trials
88 conducted in our laboratory. Study 2 extends Study 1 by employing similar ET and AC programs
89 and actigraphy-based period measurement. Additionally, Study 2 introduced
90 polysomnography and questionnaires for enhanced sleep analysis, following the methods
91 used in Study 1. Both studies were approved by the National Ethics Committee for Research
92 in Sciences and Techniques of Physical Activity and Sports (CERSTAPS, n°#2015_06_07_10)
93 and the National Ethics Committee (Comité de Protection des Personnes, CPP Nord Ouest III,
94 n°ID-RCB: 2020-A01578-31), by French regulations.

95 Participant

96 Forty-three volunteers, aged between 61 and 79 years, were recruited through local
97 advertisements and media reports. All participants underwent a screening visit with the
98 following inclusion criteria: ≥ 60 years old, both males and females, independently living in
99 Normandy. Exclusion criteria included the absence of computing equipment (computer, web
100 connection, and webcam); illiteracy; signs of cognitive impairment (MMSE score below 26/30);
101 more than one and half hours per week of supervised physical exercise; uncontrolled
102 psychiatric, neurologic, and endocrine conditions; uncontrolled cardiovascular conditions
103 (determined during a VO2 peak test); current or prior shift work history; diagnosed sleep
104 disorders; extreme circadian typology; vestibular or hearing conditions; psychotropic or
105 negatively chronotropic medical treatments; as well as alcohol, drug or caffeine abuse.
106 Detailed written and oral information about the trial was provided to participants by the
107 researchers. Participation was voluntary and participants could withdraw at any time. Eligible
108 participants were allocated to one of the groups using simple randomization from a computer-
109 generated numbered sequence. Participants were assigned a number in the order of their
110 registration inclusion in the study.
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Two participants from the ET group of study 1, four from the ET group of study 2, and two from the AC group of study 2 discontinued participation in the program due to health reasons unrelated to the study or family obligations (Figure 1). Actigraphy data from one participant in the AC group of study 2 was excluded from the actigraphy analysis due to non-compliance with instructions (failure to wear the actigraph during the night).

<figure><img src="image_18.png" /><figcaption>**Figure 1:** Participant flow chart. (A) Participant flow chart of study 1. (B) Participant flow chart of study 2.</figcaption></figure>

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Interventions

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Both interventions were delivered via videoconference using a secure web-based platform named SAPATIC (Health and Adapted Physical Activities using Information and Communication Technologies developed by Mooven, Montpellier, France). Participants were able to connect to the platform from home and had access to their intervention schedule and follow-up. The videoconferencing session were conducted in real time and were not recorded. A certified adapted physical activity coach led all the sessions live. Participants from both groups joined the same videoconference sessions simultaneously, according to their assigned group. The same coach supervised all participants within each group during each session. Retention rate was monitored based on participant presence during these live videoconference sessions.

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**Exercise training (ET) program.** For both studies, an ET program designed following the guidelines established by the World Health Organization (WHO) and the American College of Sports Medicine (ACSM) [30] was conducted via videoconferencing at home. Training sessions were scheduled in the early afternoon and were administered by physical trainers, who were blinded to the study outcomes. The ET program was quite similar in content for the 2 studies. Study 1 proposed one-hour training twice a week for 16 weeks, while Study 2 proposed one-hour training three times a week for 12 weeks. The programs aimed to enhance muscle function, aerobic capacity and maintain autonomy. Each 60-minute training session comprised a verbal contact sequence; warm-up exercises (5–10 min); whole body session (40 min); a cool-down period (5–10 min); and finally, a verbal feedback sequence.
The aerobic component of the training involved exercises with steppers and heart rate (HR) monitoring, while the resistance exercises included various types focusing on whole-body muscle. The target heart rate increased over the weeks, starting at 60% of their reserve heart rate (also determined during the VO₂ peak test) and reaching 75% at the end of the intervention. Perceived pain, fatigue, and well-being were evaluated (visual analogue scales) at each session before, during, and after sessions. Further details regarding the ET program and technological development are described elsewhere [31].

**Health education program (active control, AC).** The inclusion of an AC group aimed to mitigate any potential influence of social contact and motivational factors on performance differences between the groups. The participants in the AC group attended a one-hour health education session each week for either 12 weeks (study 1) or 16 weeks (study 2). These videoconference sessions covered topics such as the benefits of physical activity, healthy lifestyles (including sleep habits and dietary balance), and cardiovascular risk factors.

## 151 Measurements

Three evaluation sessions were organized in the laboratory for both groups: one for inclusion and randomization (visit 1), one for pre-intervention tests (visit 2), and another one for post-intervention tests (visit 3) (Figure 2). During visit 1, participants underwent a medical check-up to verify eligibility criteria and collect socio-demographic data. Circadian rhythms using actigraphy were assessed during 7 days before the visit 2 and during 7 days after the visit 3 for all participants. Only the participants of Study 2 underwent, in addition to actigraphy, polysomnography, and sleep questionnaires during visits 2 and 3 to assess their sleep quality and quantity.

<figure><img src="image_19.png" /><figcaption>**Figure 2:** Experimental flow diagram. PT: Physical Training; AC: Active Control.</figcaption></figure>

**Actigraphy.** Actigraphy involves recording body movements using a wrist-worn triaxial accelerometer. All participants wore an actigraph (Motion Watch 8, CamNTech Ltd, Cambridge, UK) on their non-dominant wrist for the entire 7 days. Participants were asked to report any events during the study, such as device removal from the wrist.

**Polysomnography.** Participants underwent a polysomnography (PSG) recording at home using a portable device (Embletta® MPR Sleep System, NATUS, Orlando, USA). Ten electrodes were placed over the scalp according to the international 10 – 20 system. This montage also
included an electrooculogram, an electrocardiogram, and a chin electromyogram. Respiratory movements, airflow, and oxygen saturation were recorded with abdominal and thoracic belts, nasal and oral thermistors, and finger pulse oximetry. In cases where moderate to severe Obstructive Sleep Apnea (OSA) was retrospectively observed in the PSG recordings, participants were informed and strongly encouraged to consult a sleep medicine clinic for formal evaluation and possible treatment.

**Sleep questionnaires.** Sleep quality and quantity were evaluated by self-report questionnaires. Sleep quality and disturbances over the last month were evaluated using the Pittsburgh Sleep Quality Index (PSQI) [32]. Subject level of daytime sleepiness was assessed by an Epworth Sleepiness Scale (ESS) [33] and insomnia over the last month was assessed by an Insomnia Severity Index (ISI) [34].

## Data analysis

**Actigraphy.** Circadian rhythm analysis was conducted using the cosinor method with raw data from the activity-rest cycle in R Studio software (www.r-project.org) [35]. We applied generalized nonlinear least squares regression with a 24-hour period to assess the condition effect on actigraph variables. The common cosinor function is a mathematical technique used to analyze and quantify rhythmic biological data. It involves fitting a cosine function to time-series data, allowing for the characterization of periodic patterns within the data. In a typical cosinor analysis, data points are collected over a period, and the cosinor function is fitted to these data to estimate the acrophase (the peak of the cosine curve), mesor (average value of measurements (Midline Estimating Statistic Of Rhythm)), amplitude (half the difference between maximum and minimum values for the period) and phase (the timing of the acrophase).

$$Y(t) = mesor + ampl \cos \left( \frac{(t - phase) * 2.\pi}{p} \right)$$

Briefly, the mesor represents the average activity level, the amplitude reflects the strength of the rhythm, and the acrophase indicates the timing of peak activity. Changes in these variables help interpret shifts or disruptions in circadian patterns under different conditions.

**Polysomnography.** Sleep data were analyzed using RemLogic software. PSG recordings were manually scored in 30-s epochs following the recommended standard criteria of the American Academy of Sleep Medicine [36]. To ensure the accuracy and reliability of the PSG data, all sleep recordings were reviewed through a double scoring process by trained technicians. Sleep analyses calculated standard sleep parameters, including total sleep time (TST, min), sleep onset latency (SOL, min), wake after sleep onset (WASO, min), sleep efficiency (SE, %), percentage of time spent in each sleep stage (N1, N2, Slow-Wave Sleep (SWS) and Rapid-Eye Movement (REM) sleep, %) of TST, and apnea-hypopnea index (AHI).
**Sleep questionnaires.** The PSQI score is the sum of its components, with 5 or higher indicating poor sleep quality [32]. The ESS score, ranging from 0 to 24, assesses daytime sleepiness, with higher scores indicating greater sleepiness [33]. The ISI score, ranging from 0 to 28, classifies insomnia as follows: 0-7 (no insomnia), 8-14 (sub-threshold), 15-21 (moderate), and 22-28 (severe) [34].

**Statistical Analysis**

To provide a more comprehensive analysis, data from two independent datasets were pooled. The datasets were deemed sufficiently similar to justify their combination for statistical analyses, based on comparable intervention, methodologies, participant characteristics, and outcome measures.

To standardize outcomes across studies and minimizing the influence of possible baseline differences [37], Z-score changes were calculated for circadian rhythms and sleep quality outcomes (actigraphy, polysomnography, and questionnaires). Z-score change provides a reliable measure of the intervention effect size, enabling the comparison of experimental groups using a standardized method. Z-scores were computed from raw scores by subtracting individual scores from the global mean (pre- and post-combined) and divided by the global standard deviation (pre- and post-combined). Z-score changes were then calculated by subtracting the pre-intervention Z-score from the post-intervention Z-score [37].

Independent Student's t-tests were performed on the Z-score changes to compare the two groups when the data exhibited normal distribution (verified by the Shapiro-Wilk test). For non-normally distributed data, non-parametric Mann-Whitney tests were employed. Data are presented as Means ± SD. The threshold for statistical significance was set at p < 0.05. All statistical analyses were conducted using JASP (version 0.16.3, JASP Team, Amsterdam, The Netherlands).

**RESULTS**

The characteristics of the participants are presented in **Table 1**. Both intervention groups demonstrated 100% adherence, as participants completed all sessions. The retention rate was calculated based on the number of sessions completed relative to the total number of sessions targeted.

<table><caption>**Table 1.** Participants characteristics (Mean ± SD).</caption><thead><tr><th></th><th>ET group (n=21)</th><th>AC group (n=22)</th></tr></thead><tbody><tr><td>Proportion of women (%)</td><td>76.9</td><td>75</td></tr><tr><td>Age (years)</td><td>70.2 ± 5.3</td><td>70.1 ± 6.2</td></tr><tr><td>Weight (kg)</td><td>75.3 ± 13.1</td><td>76.0 ± 17.4</td></tr><tr><td>Height (cm)</td><td>164.2 ± 8.1</td><td>165.9 ± 8.1</td></tr><tr><td>BMI (kg/m²)</td><td>27.9 ± 4.6</td><td>27.5 ± 5.1</td></tr><tr><td>Retention rate (%)</td><td>100</td><td>100</td></tr></tbody></table>
**Actigraphy.** Significant differences between groups were observed in Z-score change in mesor (+0.5 ± 0.4 in ET vs. -0.1 ± 0.7 in AC; p<.001) and amplitude (+0.5 ± 0.7 in ET vs. -0.2 ± 0.8 in AC; p=.009) values (Table 2). These results are illustrated in Figure 3. No significant difference was found in phase values.

<table><caption>**Table 2.** Values (Mean ± SD) of circadian rhythms of activity-rest cycle calculated by the COSINOR method. * Indicates a significant difference in Z-score change between the two groups with p<0.05.</caption><thead><tr><th rowspan="2"></th><th colspan="3">ET group (n=21)</th><th colspan="3">AC group (n=21)</th><th rowspan="2">p</th></tr><tr><th>Pre-</th><th>Post-</th><th>Z Score Change</th><th>Pre-</th><th>Post-</th><th>Z Score Change</th></tr></thead><tbody><tr><td>Mesor (mvt.min<sup>-1</sup>)</td><td>151.3 ± 40.1</td><td>175.5 ± 42.7</td><td>+ 0.5 ± 0.4</td><td>150.7 ± 56.9</td><td>145.7 ± 55.5</td><td>- 0.1 ± 0.7</td><td>< 0.001*</td></tr><tr><td>Amplitude (mvt.min<sup>-1</sup>)</td><td>139.4 ± 35.4</td><td>160.3 ± 41.8</td><td>+ 0.5 ± 0.7</td><td>141.7 ± 49.3</td><td>134.4 ± 55.8</td><td>- 0.2 ± 0.8</td><td>0.009*</td></tr><tr><td>Phase (h)</td><td>14.2 ± 0.8</td><td>14.3 ± 0.7</td><td>+ 0.2 ± 0.8</td><td>14.0 ± 1.0</td><td>14.1 ± 1.0</td><td>+ 0.2 ± 0.6</td><td>0.980</td></tr></tbody></table>

<figure><img src="image_20.png" /><figcaption>**Figure 3: Circadian rhythm cosinor curves.** Activity COSINOR curves for ET group (light gray corresponds to pre-intervention measure and deep gray to post-intervention measure) and AC group (light gray corresponds to pre-intervention measure and deep gray to post-intervention measure). The dashed lines correspond to the MESOR values.</figcaption></figure>

**Polysomnography.** All results related to the analysis of sleep parameters are presented in Table 3. Significant differences in Z-score change between groups were observed for SE (+0.4 ± 0.4 in ET vs. +0.5 ± 1.1 in AC; p=.039) and %N1 (-0.7 ± 0.9 in ET vs. +0.1 ± 0.6 in AC; p=.044) values. A strong downward trend in WASO (-0.4 ± 0.4 in ET vs. +0.5 ± 1.1 in AC; p=0.052) was observed in the Z-score change of the ET group compared to the AC group. No significant differences were found in Z-score change for TST, SOL, %N2, %SWS, %REM, and AHI values.
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**Sleep questionnaires.** All results related to self-reported sleep questionnaire scores are summarized in **Table 3**. A significant difference in Z-score change was observed for ISI scores between the two groups (-1.4 ± 1.0 in ET vs. -0.2 ± 1.2 in AC; p=.014). No significant differences were found for PSQI and ESS values.

<table><caption>**Table 3.** Measurements of objective and subjective sleep parameters (Mean ± SD) for total sleep time (TST), sleep onset latency (SOL), wake after sleep onset (WASO), sleep efficiency (SE), percentage of stage 1, 2, 3, and sleep paradox of sleep (%N1, %N2, %SWS, %REM), apnea and hypopnea index (AHI), Pittsburgh Sleep Quality Index (PSQI), Epworth Sleepiness Scale (ESS), and Insomnia Severity Index (ISI). * Indicates a significant difference in Z-score change between the two groups with p<0.05.</caption><thead><tr><th rowspan="2">N1</th><th colspan="3">ET group (n=8)</th><th colspan="3">AC group (n=9)</th><th rowspan="2">p</th></tr><tr><th>Pre-</th><th>Post-</th><th>Z Score Change</th><th>Pre-</th><th>Post-</th><th>Z Score Change</th></tr></thead><tbody><tr><td>TST (min)</td><td>382.5 ± 43.4</td><td>384.4 ± 70.0</td><td>+ 0.0 ± 1.4</td><td>381.3 ± 53.5</td><td>354.2 ± 50.9</td><td>- 0.4 ± 0.7</td><td>0.405</td></tr><tr><td>SOL (min)</td><td>10.0 ± 8.4</td><td>7.3 ± 6.1</td><td>- 0.5 ± 1.0</td><td>10.8 ± 9.3</td><td>8.0 ± 5.0</td><td>- 0.4 ± 1.5</td><td>0.965</td></tr><tr><td>WASO (min)</td><td>81.6 ± 48.6</td><td>59.3 ± 32.1</td><td><strong>- 0.4 ± 0.4</strong></td><td>86.3 ± 31.6</td><td>113.9 ± 80.4</td><td><strong>+ 0.5 ± 1.1</strong></td><td><strong>0.052</strong></td></tr><tr><td>SE (%)</td><td>81.3 ± 7.9</td><td>85.6 ± 5.1</td><td><strong>+ 0.4 ± 0.4</strong></td><td>79.9 ± 5.4</td><td>75.5 ± 13.8</td><td><strong>- 0.4 ± 1.0</strong></td><td><strong>0.039*</strong></td></tr><tr><td>%N1</td><td>11.0 ± 6.6</td><td>7.8 ± 3.4</td><td><strong>- 0.7 ± 0.9</strong></td><td>8.6 ± 3.0</td><td>9.0 ± 4.8</td><td><strong>+ 0.1 ± 0.6</strong></td><td><strong>0.044*</strong></td></tr><tr><td>%N2</td><td>38.8 ± 5.2</td><td>36.8 ± 5.7</td><td>- 0.3 ± 0.8</td><td>42.9 ± 5.1</td><td>38.7 ± 6.2</td><td>- 0.7 ± 1.3</td><td>0.529</td></tr><tr><td>%SWS</td><td>30.9 ± 6.0</td><td>35.4 ± 6.1</td><td>+ 0.7 ± 0.7</td><td>32.4 ± 3.7</td><td>36.3 ± 7.7</td><td>+ 0.6 ± 1.1</td><td>0.596</td></tr><tr><td>%REM</td><td>19.3 ± 8.7</td><td>20.0 ± 4.5</td><td>+ 0.1 ± 1.3</td><td>16.2 ± 5.0</td><td>15.9 ± 4.1</td><td>- 0.1 ± 0.3</td><td>0.744</td></tr><tr><td>AHI</td><td>15.2 ± 7.5</td><td>12.3 ± 10.3</td><td>- 0.3 ± 0.9</td><td>20.7 ± 9.5</td><td>19.6 ± 7.5</td><td>- 0.1 ± 0.7</td><td>0.609</td></tr><tr><td>PSQI</td><td>9.9 ± 2.9</td><td>6.3 ± 3.3</td><td>- 1.2 ± 1.5</td><td>10.9 ± 1.7</td><td>9.0 ± 1.9</td><td>- 0.6 ± 0.7</td><td>0.306</td></tr><tr><td>ISI</td><td>13.8 ± 4.1</td><td>7.5 ± 4.2</td><td><strong>- 1.4 ± 1.0</strong></td><td>13.2 ± 4.7</td><td>12.4 ± 2.2</td><td><strong>- 0.2 ± 1.2</strong></td><td><strong>0.014*</strong></td></tr><tr><td>ESS</td><td>6.5 ± 6.6</td><td>8.1 ± 6.7</td><td>+ 0.3 ± 0.6</td><td>6.0 ± 5.0</td><td>5.1 ± 3.2</td><td>- 0.2 ± 0.4</td><td>0.079</td></tr></tbody></table>

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The main aim of this study was to evaluate the impact of a home-based videoconferencing ET program on circadian rhythms and sleep quality in healthy older adults. Significant differences between groups were observed in Z-score change in activity average level (mesor) and amplitude, indicating an improvement in circadian rhythms for the ET program compared to the AC group. Significant differences between groups in Z-score change were observed in SE, %N1, and ISI score values, suggesting an improvement in objective and subjective sleep quality for the ET program compared to the AC group.

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The present study demonstrates that a home-based ET program can significantly enhance circadian rhythms in healthy older adults. Specifically, the intervention resulted in significant improvements in mesor and amplitude values in comparison with the AC group, suggesting a more robust circadian rhythmicity and enhanced daily motor activity following the ET program. In practical terms, an increase in mesor reflects a higher overall level of activity across the 24-hour period, while a higher amplitude indicates a clearer distinction between active and rest phases, pointing to a better-regulated circadian rhythm. To our knowledge, our findings align with previous research of Ensrud et al. which showed that regular exercise can improve sleep,
264 as measured by actigraphy, by increasing day-to-day activity levels, as poor functional abilities
265 were associated with poor sleep in frail older adults compared to robust older adults [38]. In
266 an observational study, Dupont Rocher et al. (2016) also observed that older adults with
267 higher physical capacity had more pronounced circadian rhythms (temperature, activity-rest
268 cycle, alertness) [39]. Exercise regulates circadian rhythms by enhancing clock gene expression
269 in the SCN, aligning them with external cues, and improving internal synchronization [40,41].
270 To explain our results, various hypotheses have been proposed regarding clock resetting by
271 non-photic stimuli. Exercise affects the circadian pacemaker in the SCN via the geniculo-
272 hypothalamic tract [42], and neurotransmitters like serotonin mediate its wakefulness-
273 promoting effects [43,44]. The increase in body temperature induced by exercise may serve
274 as an input to the circadian pacemaker [39,45]. Additionally, the regular timing of physical
275 activity can act as a social synchronizer, helping to stabilize circadian rhythms [46]. A daily
276 exercise routine sends strong signals to the internal clock, promoting better mood, increased
277 energy, and a reduction in symptoms of sleep disorders [46-48].

278 We didn't observe any difference in the Z-score change of phase values, indicating that the
279 early afternoon ET intervention did not exert influence on this parameter. This finding is
280 consistent with existing literature, suggesting that it is physical training sessions conducted in
281 the early morning or evening in young and older adults that primarily impact the phase rhythm
282 [49-52]. The choice of early afternoon for the exercise sessions was based on practical
283 feasibility and participant availability. This time window also corresponds to a natural peak in
284 circadian-driven alertness and body temperature, offering an opportunity to reinforce, rather
285 than shift, circadian rhythms in older adults [53].

286 The present study demonstrates that a remote ET program significantly improves sleep quality,
287 as measured by polysomnography, in healthy older adults. Following a 12-week ET program,
288 there were significant improvements in sleep quality with higher sleep efficiency and lower
289 stage 1 sleep percentage values compared to the AC group. A strong downward trend in
290 wakefulness after sleep onset was observed. These results indicate an improvement in sleep
291 quality and a reduction of sleep fragmentation after an ET program among our population.
292 These findings are consistent with previous research. One study reported that a 12-month
293 exercise program improved sleep quality, reducing N1 sleep and increasing N2 sleep in older
294 adults compared to a control group [54]. Another study found that 16 weeks of endurance
295 training improved sleep efficiency and SWS while reducing light sleep [55]. The mechanisms
296 through which exercise improves sleep quality are multifaceted and may include changes in
297 circadian rhythms through changes in rest-activity rhythms, body temperature, heart rate
298 variability, growth hormone secretion, and overall fitness improvements [56,57]. Also,
299 exercise may induce relaxation, and preparation of the body and mind for rest [58,59].

300 The ET program did not significantly affect Z-score changes of SOL, TST, %N2, %SWS, %REM,
301 and AHI compared to the AC group, possibly due to the high AHI in our sample. It is worth
302 noting that the percentage of slow-wave sleep (SWS) observed in our older participants may
303

appear unusually high, reaching approximately 30% of total sleep time. While this exceeds
304
typical averages for this age group, which are usually closer to 20%, it may reflect the
305
characteristics of our sample—healthy and physically active older adults—who may exhibit
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sleep patterns associated with successful aging. Moreover, OSA, caused by upper airway
307
obstruction during sleep, is more prevalent in older adults and disrupts circadian rhythms and
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sleep architecture [60,61]. Exercise alone is insufficient to regulate sleep apneas, which are
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rooted in nervous control of respiration [62].

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We did observe a significant difference in the Z-score changes of insomnia scores between the two groups. After the ET group, participants showed a greater decrease in ISI score than the AC group. These results indicate a reduction in feelings of insomnia following the ET program and are consistent with the previous literature [25]. Contrary to the literature, no significant differences were observed in PSQI and ESS scores between the groups [24,25]. This pattern—improvement in ISI but not in PSQI or ESS—highlights the complexity of evaluating sleep and the frequent discrepancy between different types of subjective assessments. Interestingly, while objective measures like increased sleep efficiency and a reduced proportion of light sleep (N1) showed clear improvements, these were not mirrored by global subjective tools. Questionnaires like the ISI are more focused and may be better suited to detect changes specifically related to insomnia, such as difficulties with sleep onset or maintenance, which are often responsive to physical activity. In contrast, the PSQI assesses multiple dimensions of sleep over a longer period, and the ESS reflects broader factors affecting daytime alertness, which may not be directly influenced by exercise alone. Moreover, subjective perception of sleep is influenced by various psychological factors such as mood, stress, and expectations, which can sometimes overshadow physiological improvements. However, Melancon et al. found significant improvements for two objectively measured sleep outcomes (NREM and wake time after sleep onset), but not for any subjective outcome [55]. This divergence between subjective questionnaires further underscores the importance of using both subjective and objective tools in sleep research, as they capture distinct dimensions of sleep experience.

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The study's retention rate and results highlight the effectiveness of remote physical training
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via videoconferencing for improving sleep and circadian rhythms in older adults. Given the
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isolation that limits physical activity in this population [26,63], videoconferencing offers a
334
valuable solution [27,28,64]. With a 100% retention rate, these real-time programs are well-
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received and provide an accessible way to overcome barriers to physical activity, thereby
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contributing to improved circadian rhythms and reducing the severity of insomnia.

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This study has some important limitations regarding potential confounding factors related to
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undiagnosed or subclinical sleep disorders common in older adults. While we excluded
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participants with diagnosed sleep disorders and uncontrolled psychiatric or neurological
340
conditions, it is possible that undiagnosed OSA, Restless Legs Syndrome, or early neurological
341
disorders (e.g., prodromal Parkinson's disease) were present in our sample. These conditions

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can influence polysomnography measures such as wake after sleep onset and sleep efficiency, as well as actigraphy parameters, potentially adding variability or bias to our findings. High AHI values in some participants support the likelihood of undiagnosed OSA contributing to the results. Furthermore, insomnia symptoms assessed by the ISI at baseline might reflect a mix of chronic insomnia and other sleep disturbances, complicating interpretation of intervention effects. Future studies should consider more comprehensive screening for common sleep disorders, such as polysomnography screening, in older adults to better isolate the effects of exercise interventions.

This study presents additional limitations that warrant consideration. We lack data on the percentage of light exposure, preventing us from differentiating potential light effects from more generalized exercise effects on sleep quality. Incorporating measures of light exposure in future studies could help elucidate its impact on sleep outcomes. The small sample size of the polysomnography sleep analysis (n = 8) may limit the power of our analysis and suggest that larger sample sizes are needed to detect additional changes in this population accurately. Moreover, while the combination of data from two studies with comparable methodologies and the use of Z-score normalization was methodologically justified, this approach introduces potential sources of variability. Therefore, findings related to specific objective and subjective sleep parameters should be considered preliminary and require confirmation in larger, independent cohorts using a single standardized protocol and in other, more complex clinical populations. Finally, it is important to note that the two studies were conducted five years apart. During this period, external events – most notably the COVID-10 pandemic – may have influenced participant behavior, sleep patterns, or psychological well-being. While we used Z-Score normalization to control for inter-study variability, temp temporal gap remains a potential source of confounding and should be consider when interpreting the results.

## Conclusion

This trial is the first to evaluate the effect of a home-based videoconferencing ET program on circadian rhythms and sleep in healthy older adults. As with face-to-face programs, our remote videoconferencing ET program improved sleep quality and rest-activity rhythms in healthy older adults. This suggests that remote ET programs can positively influence the activity-rest cycle, contributing to enhanced daily motor activity among older adults, and the objective and subjective sleep quality. These results emphasize the potential of remote physical training interventions, delivered through videoconferencing, as an optimal solution for older adults. However, findings on objective and subjective sleep parameters are preliminary and need confirmation in larger, standardized studies.

**Funding:** The Study 1 project was funded under the European Innovation Partnership for Active and Healthy Aging (EIPAHA), in response to the 5th Ambient Assisted Living call for projects (AAL-2012-call5) ANR-12-AALI-0004-03. Study 2 project was co-financed by the Normandy County Council and the European Union in the framework of the ERDF-ESF operational program 2014-2020 (ID: 19P02964).
381 **Author Contributions:** Conceived and designed the experiments: GQ, AG, LB, NB, AL.
382 Performed the experiments: EM, SR, AL, and LB. Analyzed the data: EM, LB, and NB. Wrote
383 the paper: EM. All authors have read and agreed to the published version of the manuscript.

384 **Ethics Statement:** All participants provided written informed consent to take part in the study,
385 which was conducted in line with the Declaration of Helsinki and approved by the National
386 Ethics Committee (CPP Nord Ouest III, n°ID-RCB: 2020-A01578-31; CERSTAPS,
387 n°#2015_06_07_10).

388 **Declaration of Conflicts of Interest:** None declared.

389 **Data Availability Statement:** The data that support the findings of this study are available
390 from the corresponding author upon reasonable request.

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**Declaration of interests**

[x] The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

[ ] The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:

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