This study tested a no-cost, drug-free technique to promote sleep for Veterans and their family members. The technique, hand self-shiatsu (HSS), had promising outcomes in other studies with chronic pain patients and young athletes after concussion. HSS is easy to learn, takes only 10–15 minutes to perform before bed, requires no equipment, and is best done once in bed for the night. The sleep and daytime fatigue of 30 people who were taught HSS and 20 who were not were compared across a two-month period. The two groups were similar in age and gender. The self-report measures showed that people who did HSS reported less daytime fatigue and less sleep disturbance than those who did not. Also, in interviews at the end of the study, participants were very favorable about how easy and potentially useful HSS was. They also commented on the benefit of feeling more in control of their sleep. Although the study has limitations, the findings are promising. A HSS educational video, handouts, and app are available for free at

Introduction: The determinants of Veterans’ and their family members’ health and well-being are compromised by sleep deficiency (SD). The use of long-term drug therapies for treatment is controversial, and the evidence to support positive outcomes is limited. Instead, guidelines recommend non-pharmacological sleep interventions for SD. Hand self-shiatsu (HSS) is a drug-free, pragmatic, easy-to-learn self-management technique that provides patients with an intervention they can actively control, thus contributing to feelings of self-efficacy. The objective of this work was to examine whether a prescribed HSS intervention would result in improved objectively and subjectively measured sleep. Methods: This was a non-randomized controlled study. Objective (actigraphy) measures and standardized self-report questionnaires were applied at baseline and at four and eight weeks post-intervention. Participants also completed a detailed sleep log. Results: No significant differences were found in the actigraphy sleep dimensions across the three measurement time points in either the intervention or the control group. With respect to the self-report measures, a significant change was detected for sleep disturbance (χ22 = 10 [n = 25], p = 0.007) for the intervention group, and 77% stated they would recommend HSS to others. A significant change in two self-report measures was noted in the control group, a potential artifact of the sub-optimal recruitment to this group imposed by the COVID-19 restrictions. Discussion: Although actigraphy data did not support the hypothesis, the self-report measures and qualitative information from participants’ end-of-study interviews indicated endorsement of HSS for the management of sleep difficulties and increased self-efficacy.

Introduction : Les déterminants de la santé et du bien-être des vétérans et des membres de leur famille sont compromis par le manque de sommeil (MS). Les avantages des pharmacothérapies à long terme sont controversés à cet égard, et les données sont limitées pour appuyer des résultats positifs. Des stratégies d’interventions non pharmacologiques pour soulager le MS sont plutôt recommandées. L’auto-shiatsu des mains (ASM) est une technique d’autogestion pragmatique, facile à apprendre et non pharmaceutique que les patients peuvent contrôler activement, contribuant ainsi à leur sentiment d’autoefficacité. Les présents travaux visaient à examiner si la prescription d’une intervention d’ASM améliore le sommeil, selon des mesures objectives et subjectives. Méthodologie : Dans cette étude témoin non aléatoire, les chercheurs ont utilisé des mesures objectives (actigraphie) et des questionnaires d’autodéclaration standardisés en début d’étude, puis quatre et huit semaines après l’intervention. Les participants ont également rempli un journal de sommeil détaillé. Résultats : Il n’y avait pas de différence significative des dimensions du sommeil à l’actigraphie aux trois points de mesure dans le groupe d’intervention et dans le groupe témoin. Pour ce qui est mesures d’autodéclaration, les chercheurs ont décelé un changement important en matière de perturbations du sommeil (χ22 = 10 [n = 25], p = 0,007) dans le groupe d’intervention, et 77 % ont déclaré qu’ils recommanderaient l’ASM à d’autres. Les chercheurs ont constaté un changement important dans deux mesures autodéclarées du groupe témoin, une conséquence potentielle du recrutement sous-optimal au sein de ce groupe, imposé par les restrictions de la COVID-19. Discussion : Les données d’actigraphie n’ont pas appuyé l’hypothèse, mais les mesures autodéclarées et l’information qualitative tirée des entrevues de fin d’étude des participants indiquent l’approbation de l’ASM pour prendre en charge les troubles du sommeil et accroître l’autoefficacité.

Restorative sleep is critical for optimal brain function and overall health. Sleep deficiency (SD), defined by the National Centre on Sleep Disorders Research as “too little sleep, poor quality sleep, or sleep problems including diagnosed sleep disorders,”1 is a growing problem for people of all ages, and it has a significant impact on many aspects of military members’ and Veterans’ lives.2 It has been shown to be associated with decreased insight, challenges with attentional and executive function, such as problem solving and new learning, risk of falls, and motor vehicle accidents.3 It has also been linked with increased risk of age-related illnesses, such as diabetes, cancer, hypertension, stroke and other cardiac diseases,4 depression,5 chronic pain,6 cardiovascular disease,7 and dementia.8 Moreover, recent evidence has suggested that these conditions do not simply occur in parallel, but that a bidirectional relationship exists between SD and many chronic health conditions. There is thus a negative cycle of worsening disease symptoms contributing to inability to sleep and of sleep loss in turn increasing the seriousness of symptoms of both physical and mental health conditions.

Veterans and active military members, and their intimate partners, experience SD at higher rates than the general population.2,911 Insomnia is a frequent, unrelenting, and debilitating complaint, both during and after deployment, and it does not necessarily resolve with transition to civilian life.12 The determinants of Veterans’ well-being,13 and indeed all aspects of life that Veterans and their families value and find meaningful (such as friendships, employment, social activity, and feelings of well-being and control in life), are compromised by SD.2,14

The underlying cause of SD is multi-factorial. It involves environmental and social influences, comorbid physical and mental health problems, individual habits, beliefs, and lifestyle choices, pre-enlistment sleep quality, and emotional and physical trauma experienced before and in the course of service.2,15 The benefit of ongoing drug therapies in the treatment of SD is controversial, and the evidence to support positive outcomes in long-term use,16 particularly during deployment, is limited.17 In addition, medication side effects have been noted, including impairments in cognition, insight, and new learning.18 Short courses of corrective sleep medication, and more long-term strategies of non-pharma cological sleep interventions for SD, are recommended instead.19

An important consideration in any intervention is its acceptability to, and usability by, the patient. Hand self-shiatsu (HSS) is a drug-free, pragmatic, easy-to-learn self-management technique that provides patients with an intervention they actively control, and it can thus contribute to feelings of self-efficacy. Namikoshi shiatsu, on which this HSS intervention is based, involves comfortable pressure on established points on the body related to anatomy and physiology.20 Although research is limited, aligning with findings from the similar modality of acupressure, shiatsu may exert a sleep-positive biomedical influence related to improved blood circulation, reduced muscle tension, and possible endogenous release of serotonin.21 Shiatsu is based on the principles of restoring and balancing body energy and reducing stress that underpin traditional Chinese medicine.22 Shiatsu involves applying pressure with the fingertip to specific points aligned with bodily function and health. Pressure-point interventions such as shiatsu and acupressure are suggested to trigger physiological changes such as increased blood circulation, reduced muscle tension, and increased production of serotonin.23 Research is limited, but small studies have reported that shiatsu can be effective for headache, low-back pain, and mental health problems.24 A small body of outcomes research specific to sleep benefits of shiatsu or acupressure has had promising findings.2532 More recently, pilot studies of hand shiatsu to promote sleep in which patients apply the technique themselves have emerged in the literature involving adults with chronic pain and young athletes with sport-related concussion.33,34

Because the HSS technique is quite specific, a sustained degree of concentration is required. Brain functioning and attention research have demonstrated that high concentration prevents one from simultaneously attending to other thoughts.35 Other cognitive interventions to decrease rumination in persons with insomnia have been shown to be effective.36 Congruent with these types of cognitive approaches, it was theorized that the active concentration required to carry out HSS may supplant negative sleep inhibiting thoughts and emotions. HSS aligns with the evidence-based principles of self-management and can be practised in the patient’s unique biopsychosocial and environmental context.33,34

A 2015 review found attrition rates for cognitive-behavioural therapy (CBT) can be in excess of 23%.37 Although the authors are not aware of studies comparing CBT with self-administered shiatsu, the high rate of acceptance for self-shiatsu found in previous studies contributes to the assumption that, because HSS involves concentrating on a motor activity, as opposed to personal thoughts and feelings, it may be more acceptable to persons who find the interpersonal reflection components of other cognitive therapies less desirable.33,34

The aim of this study was thus to examine whether participants in a HSS intervention group would, compared with a non-HSS control group, demonstrate improved sleep, as quantified by a wrist-worn sleep monitor (actigraph), and sleep quality and daytime fatigue, as measured by means of self-report instruments.

This was a non-randomized controlled study; arm 1 was a HSS active intervention (taught after baseline data collection), and arm 2 was a wait-list control. Objective and self-report outcomes were measured at baseline (BL) and at four weeks (FU1) and eight weeks (FU2) post-intervention. In an effort to detect potential confounding variables, participants’ beliefs about sleep, holistic health, and complementary and alternative medicine were also measured using standardized self-report tools. Qualitative questions were included at FU2 to gather information from the active intervention participants regarding their thoughts about and perspectives on HSS. The wait-list control group was offered HSS training at the end of FU2. Participants were recruited through the Royal Canadian Legion Alberta and the Northwest Territories Command and local chapters, as well as through social media. Volunteers were accepted into the study if they could complete the questionnaires in English, were Veterans (or family members of Veterans), and lived in one of the three study cities in Western Canada. The intervention occurred between Jan. 19 and May 4, 2020. The study protocol was approved by the University of Alberta Health Ethics Board (No. RES0041557).


A prescribed HSS protocol developed for previous pilot studies with different populations (available at was implemented.33,34 The protocol requires participants to self-apply pressure with the pads of their index finger, thumb, or both, for a count of three, to specific points on the dorsal and volar surfaces of the opposite hand and fingers (see website for details and demonstration video). Participants apply HSS once they are in bed, with the lights out, and ready for sleep. The sequence was applied twice and took approximately 10–15 minutes to complete. Each participant was trained in the protocol in a one-to-one session after one week of baseline sleep actigraphy and self-report data collection. Printed HSS materials (available on the website) were provided to participants to facilitate intervention fidelity. Participants were contacted by telephone in the week after HSS training to clarify questions about the technique. Follow-up assessments occurred at FU1 and FU2.

Variables and measures

Data collection consisted of various tools to measure both objective and self-reported dimensions of sleep and daytime fatigue. Potential confounding factors were also measured.

Real-time sleep data were collected using the wrist-worn ActiSleep monitor (Actigraph LLC, Pensacola, FL). Data were uploaded and analyzed using proprietary software. Actigraphy is considered to be a reliable and valid assessment tool and is widely used in sleep research.38 Participants in the active intervention group wore the device for seven days at BL (before HSS training) and at FU1 and FU2 after HSS training. The control group followed the same measurement schedule but without HSS training or use. Five actigraphy sleep dimensions were retained for analysis — sleep onset latency (minutes), total sleep time (minutes), waking after sleep onset (minutes), average number of awakenings (frequency), and average time awake (minutes). Lower scores for sleep onset latency, waking after sleep onset, average time awake, and average number of awakenings indicate improvement on these dimensions, whereas higher scores for total sleep time reflect improvement.

All participants completed a sleep log each morning and before bed for seven days at BL, FU1, and FU2. Sleep logs are commonly used to record individuals’ sleep-related information. Research with an adult population demonstrated a sensitivity of 87.93% and a specificity of 96.51% in relation to objective actigraphy data.39 In this study, the sleep log collected information about bed and wake-up times that was required for entry into the ActiSleep software before analysis of downloaded monitor data. Participants also indicated whether they had practised HSS at bedtime and during the night.

Sleep disturbance, sleep-related impairment, and fatigue were measured using three psychometrically strong scales developed by the Patient-Reported Outcomes Measurement Information System (PROMIS) program.40 For each eight-item scale, raw mean scores are converted to standardized t-scores with a mean of 50 and a standard deviation of 10. The measures ask participants to rate the past seven days on a five-point scale. The Sleep Disturbance-Short Form 8a assesses self-reported perceptions of sleep quality, sleep depth, and restoration associated with sleep, including difficulties with getting to sleep or staying asleep, and perceptions of the adequacy and satisfaction with sleep. The Sleep-Related Impairment-Short Form 8a focuses on perceptions of alertness, sleepiness, and tiredness during usual waking hours and the perceived functional impairments during wakefulness associated with sleep problems and impaired alertness. The Fatigue-Short Form 8a measures self-reported experience of fatigue (frequency, duration, and intensity) and the impact of fatigue on physical, mental, and social activities. For all three scales, higher scores indicate worse outcomes (e.g., greater sleep disturbance, greater sleep-related impairment, and greater fatigue).

Participants also completed the Pittsburgh Sleep Quality Index (PSQI), a widely used, psychometrically strong self-report measure that assesses sleep quality and disturbances over the previous month.41 Nineteen individual items generate seven component scores —subjective sleep quality, sleep latency and duration, habitual (usual) sleep efficiency, sleep disturbances, use of sleep medications, and daytime dysfunction. The sum of the scores for the seven components yields a global score with a possible range of 0–21 points, with higher scores signifying poorer sleep. A global score of five or greater indicates poor sleep quality.

The seven-item Flinders Fatigue Scale,42 a valid and reliable instrument that measures daytime fatigue experienced over the past two weeks, was also used. Six questions are presented in Likert format, with responses ranging from one (not at all) to four (extremely or entirely). The questions include how problematic fatigue is to the individual, the consequences of fatigue, its frequency and severity, and patients’ perception of their fatigue’s association with sleep. Total fatigue is calculated as the sum of all individual items, with higher scores indicating greater fatigue.

As a proxy indicator of potential co-intervention bias that could have contributed to changes in sleep behaviour (e.g., exposure to sleep education or self-directed information seeking that occurred simultaneously with the HSS protocol), participants completed the Sleep Beliefs Scale at BL and FU2.43 This scale contains 20 statements describing select behaviours and asks participants whether each has a positive, negative, or no effect on the quality and quantity of sleep in general. Examples of belief statements include “going to bed and waking up at the same hour” and “getting up when it is difficult to fall asleep.” Participants also completed the psychometrically sound Holistic Complementary and Alternative Medicine Questionnaire (HCAMQ),44 an 11-item questionnaire measuring attitudes about holistic health and the scientific validity of complementary and alternative medicine.

At the end of the study, participants were asked questions about additional sleep strategies they may have used during the intervention, how frequently they used the HSS protocol during the study, how quickly they fell asleep while using the technique, whether the HSS steps were easy to remember, whether they felt HSS improved their sleep, and whether they would recommend HSS to others. They were also asked to identify any perceived positive or negative aspects of HSS as well as to rate HSS as a sleep improvement tool on a scale ranging from 1 to 10.

Data analysis

IBM SPSS Statistics version 26.1(IBM Corporation, Armonk, NY) was used for all analyses. The Mann-Whitney U test was used to assess between-groups differences in sex and pre-intervention scores on the potential confounding factors. Chi-square tests were used to assess potential differences in mean age between the intervention and control groups. Friedman’s test was used to analyze the variance between measurements over the three consecutive measurement points, with the significance level set at 0.025. The qualitative data were not subjected to a systematic analysis. Rather, the information gleaned was used to inform a general understanding of the acceptability and use of HSS by this population.

Fifty people were recruited into the study, with 30 assigned to the intervention group and 20 assigned to the wait-list control group. Recruitment for the wait-list control group was prematurely terminated Mar. 16, 2020, as a result of COVID-19 pandemic-related public health mandates. Outstanding data collection was completed by mail. Five people dropped out of the intervention group after FU1, and four people in the control group did not complete all the data collection forms. The mean age was 67 (range = 39–100) years for the intervention group and 64 (range = 33–86) years for the control group. Women made up 53% of the intervention group and 42% of the control group. There was no significant difference in age (p = 0.55) or sex (p = 0.64) between groups. Notably, on the basis of actigraphy data, 20.5% (19% intervention group, 22% control group) of the participants slept fewer than 6.5 hours nightly at BL.

Objective outcomes

Friedman’s test revealed no significant difference on any of the actigraphy sleep dimensions across the three measurement time points in either the intervention group or the control group (Table 1). Please note that actigraphy data were missing among participants in both groups who reported occasionally forgetting to wear the actigraph.

Sleep log

At FU1 and FU2, 44% and 40% of the intervention group participants, respectively, reported using HSS before bed between four and seven nights a week. At FU1 and FU2, 34% and 26% of these participants, respectively, reported using HSS if they woke during the night. Table 2 details participants’ adherence to the HSS protocol.


Table 1. Actigraphy results — Friedman’s test (analysis of variance)

Table 1. Actigraphy results — Friedman’s test (analysis of variance)

Dimension and group Mean scores
χ22 p*
Latency (min)
 Intervention 3.70 2.10 2.85 0.50 0.78
 Control 2.25 1.15 2.20 0.10 0.95
Total sleep time (min)
 Intervention 438.88 431.49 439.46 1.81 0.41
 Control 427.94 432.97 440.55 0.38 0.83
WaSO (frequency)
 Intervention 53.47 53.37 52.75 2.80 0.25
 Control 55.38 55.11 56.14 0.38 0.83
Awakenings (frequency)
 Intervention 14.29 14.86 15.05 2.87 0.24
 Control 13.64 13.89 13.91 2.63 0.27
ATA (min)
 Intervention 3.80 3.80 3.63 3.90 0.14
 Control 4.23 4.10 4.33 1.13 0.57

Note: Intervention group, n = 20; control group, n = 16.

*Significant at p < 0.025.

BL = baseline; FU1 = follow-up 1, FU2 = follow-up 2; WaSO = wakenings after sleep onset; ATA = average time awake.

Self-report outcomes

Results for all self-report measures were analyzed using Friedman’s test to identify changes from BL through FU1 and FU2 (Table 3). For the intervention group, a significant change was detected for Sleep Disturbance-Short Form 8a (χ22 = 10 [n = 25], p = 0.007), but not for the Sleep-Related Impairment-Short Form 8a, Fatigue-Short Form 8a, PSQI, and Flinder’s Fatigue Scale. For the control group, a significant change was detected for the Fatigue-Short Form 8a (χ22 = 7.96 [n = 16], p = 0.019) and the PSQI (χ22 = 9.44 [n = 16], p = 0.009). Analysis of the difference in scores between the intervention and control groups for each self-report measure revealed no statistical significance.

Potential confounding variables

No change was found in scores on the Sleep Beliefs Scale, indicating that the participants likely did not apply new information pertaining to conditions affecting sleep (Table 4). The HCAMQ results also indicated no significant change in scores between BL and FU2. Its two sub-scales, Acceptance of Complementary and Alternative Medicine and Support for Holistic Health Beliefs, were analyzed separately. Again, as expected, there were no changes in these scores from BL to FU 2.


Table 2. Frequency of HSS protocol completions by intervention group participants during data collection periods

Table 2. Frequency of HSS protocol completions by intervention group participants during data collection periods

Frequency n (%)
6–7 nights
 Before bed 18 (36) 15 (30)
 During night 5 (10) 4 (8)
4–5 nights
 Before bed 4 (8) 5 (10)
 During night 5 (10) 2 (4)
1–3 nights
 Before bed 2 (2) 3 (6)
 During night 7 (14) 7 (14)
0 nights
 Before bed 1 (2)
 During night 7 (14) 10 (20)
Missing 6 (12) 6 (12)

HSS = hand self-shiatsu; FU1 = follow-up 1; FU2 = follow-up 2.


Table 3. Self-report measures — Friedman’s test (analysis of variance)

Table 3. Self-report measures — Friedman’s test (analysis of variance)

Scale and group Mean ranks
χ22 p
Sleep disturbance
 Intervention 2.30 2.20 1.15 10.00 0.007*
 Control 2.19 1.88 1.94 1.00 0.61
Sleep-related impairment
 Intervention 2.36 1.74 1.90 5.51 0.06
 Control 1.97 2.19 1.84 1.03 0.60
Fatigue SF 8a
 Intervention 2.10 1.84 2.06 1.14 0.57
 Control 2.19 2.34 1.47 7.96 0.019*
 Intervention 2.29 1.94 1.77 3.79 0.15
 Control 2.10 2.43 1.47 9.44 0.009*
Flinder’s Fatigue Scale
 Intervention 2.24 1.80 1.96 2.76 0.25
 Control 2.26 2.21 1.53 6.13 0.047

Note: Intervention group, n = 25; control group, n = 16.

*Significant at p < .025.

BL = baseline; FU1 = follow-up 1; FU2 = follow-up 2; sleep disturbance = Sleep Disturbance-Short Form 8a; sleep-related impairment = Sleep-Related Impairment-Short Form 8a; SF = Short Form; PSQI = Pittsburgh Sleep Quality Index.


Table 4. Confounding variables

Table 4. Confounding variables

Sleep Beliefs Scale CAM HH HCAMHQ
Z 1.683 1.027 1.065 0.821
p* 0.09 0.30 0.29 0.41

*Using Wilcoxon test; p £ 0.05.

CAM = Acceptance of Complementary and Alternative Medicine sub-scale; HH = Support for Holistic Health Beliefs sub-scale; HCAMQ = Holistic Complementary and Alternative Medicine Questionnaire

At the completion of the study, participants responded to a brief questionnaire about their experience with HSS. Twenty-three participants (77%) stated that they would recommend HSS to others, and 13 (43%) rated the effectiveness of HSS at a 6 or more on a scale ranging from 1 to 10. An open-ended question, asking why participants thought HSS improved their sleep, yielded the following responses (among others):

HSS took my focus off of stress and problems of daily living and pain; instead it focused my mind on an activity which in itself was very relaxing.

Now I am getting more sleep and I wake up less often and get to sleep faster; I used to toss and turn all night to the point that I would have to get up and leave the bed but I am leaving less often.

Made me slow down and stay focussed on my sleep hygiene.

When asked whether they would recommend HSS to others, participants stated, “No harm, worth it to try something drug-free,” “non-invasive, natural,” “I did not feel it helped me but it may help someone else,” and “I’m sure some people would benefit; I found it a good way to wind down — sort of a meditation with deep breathing and counting my hold times.”

The primary objective of this work was to examine whether a prescribed HSS intervention would result in improved objectively and subjectively measured sleep for a population of Veterans and their family members who reported experiencing poor sleep. Although there were no statistically significant demographic differences between the groups, it was notable that more than 20% of all participants had an actigraphy sleep duration measurement of less than 6.5 hours at baseline. This is a notable finding because research has shown that a shorter duration of sleep significantly increases the risk of developing chronic health conditions, including cardiovascular disease and diabetes.45

Although there was no statistically significant change in objectively measured actigraphy measures of sleep, results from the intervention group’s subjective self-report measures demonstrated statistically significant improvement in sleep disturbance. The importance of including both objective and subjective self-report outcome measures in sleep intervention studies has been well documented in the literature.4649 Studies comparing subjective self-report and objective measures indicate that these most likely measure different constructs of restorative sleep. For example, a 2018 study of older adults attending a Veterans Administration adult day health care program in the United States concluded that “objective and subjective sleep measures may represent unique and equally important constructs in this population.”46(p. 145) Unruh and colleagues pointed out that older adults, having become acclimatized to changes in their sleep quality over time, may not always recognize sleep problems as significant.49 This may contribute to the disconnect between objective and self-report outcomes. Accordingly, the statistically significant findings for the Sleep Disturbance-Short Form 8a,40 measuring perceptions of sleep quality, sleep depth, restoration associated with sleep, difficulties with getting to sleep, staying asleep, and feelings about the adequacy and satisfaction with sleep, should not be minimized.

It appears that perceptions of restorative sleep, although not well studied, can be related to daytime functioning, depression, and other quality-of-life factors,5052 all of which are important considerations in older adults’ ability to age well. There is a significant gap in understanding of the role that subjective feelings of restorative sleep play in overall well-being, and this is an important area for future research.

The authors’ rationale for selecting HSS as the non-pharmacological intervention was that it is easy to learn, has no cost, and can contribute to participants’ feelings of self-efficacy in managing some aspects of their sleep problems. Although the authors did not directly measure ease of learning HSS and changes in sleep self-efficacy, the positive feedback from the interview at the end of the study (discussed later) seems to support that this was the case for many participants. Regarding ease of learning, participants stated that HSS was “easy, worth a try” and “simple to do; helps you get a sleep routine.” Specific statements that suggest an enhanced sense of being in control and self-efficacy included “made me slow down and stay focussed on my sleep hygiene,” “it is methodical and becomes habitual,” “helped me to stop thinking negatively,” “it becomes part of the bedtime routine,” and “I used to have fear of going to bed and now I do not feel that way.” Some participants also attributed a reduction in medication to HSS: “Gave me another resource and the prospect of not being dependent on sleep drugs,” “no medications needed,” and “I was considering switching to a stronger sleeping pill, and I’m not going to do that anymore.”

Underscoring the importance of facilitating feelings of self-efficacy (a component of resiliency for Veterans)53 is a 2018 study involving 1,118 U.S. Veterans that determined that self-efficacy had a significant buffering effect on the relationship between psychological distress and poor sleep.52 This is an emerging area of research, and Grah and colleagues highlight the need for more nuanced sleep self-efficacy outcome measures.54

Participants’ responses to the end-of-study questionnaire indicate that these older adults were receptive to this type of self-managed sleep intervention. The findings regarding perceived ease of learning, sense of control, and acceptability of the intervention are important, given their relationship to sleep insufficiency and the shortage of non-pharmacological sleep interventions. HSS is a no-cost, self-managed, pragmatic intervention that is easily learned and, by its very nature, undeniably portable. These are important considerations, and HSS may be a useful addition to practice when working with this population.


The study had several limitations; the sample size was relatively small, the authors were not able to medically screen for existing or non-diagnosed conditions such as apnea, control group recruitment was forced to terminate prematurely, adherence to the HSS technique between follow-up periods was not monitored, electronic screen time and other sleep-inhibiting activities before bedtime were not controlled for, and HSS technique fidelity was not monitored. The authors noted an unexpected statistically significant change in the control group’s scores on the fatigue scales and the PSQI self-report, but they speculate that this may reflect a placebo effect related to wearing the actigraph and completion of self-report forms. The control group had an insufficient sample size due to the premature closure of recruitment because of the COVID-19 pandemic, so caution is required with any conclusions.

Although adherence to the HSS protocol was good during the two follow-up measurement periods, it is possible that participants’ HSS use fell off between the data collection periods. The last follow-up may have been too early to identify whether the HSS practice had become a sustained habit. It is also possible that HSS is most effective as an intervention for delayed sleep onset. By FU2, only 12% of participants were using HSS four or more times a week during nighttime awakenings. It therefore cannot be determined whether HSS had an influence on wakenings after sleep onset. The outcome of HSS use during the night needs further examination. Finally, the limitations of actigraphy in collecting objective sleep data need to be acknowledged. Future studies would be enhanced through use of electroencephalography to explore changes in Theta and Delta wave activity and polysomnography collected over several nights.

1. National Center on Sleep Disorders Research [Internet]. Bethesda (MD): National Institutes of Health; 2010 [cited 2021 Mar 27]. National Institutes of Health sleep disorders research plan. Available rom: Google Scholar
2. Brown CA, Berry R, Schmidt A. Sleep and military members: emerging issues and non-pharmacological intervention. Sleep Disord. 2013;2013:160374. Medline:23956864 Google Scholar
3. van Cauter E, Spiegel K. Sleep as a mediator of the relationship between socioeconomic status and health: a hypothesis. Ann NY Acad Sci. 1999;896:25461. Medline:10681902 Google Scholar
4. Buxton OM, Marcelli E. Short and long sleep are positively associated with obesity, diabetes, hypertension, and cardiovascular disease among adults in the United States. Soc Sci Med. 2010;71(5):102736. Medline:20621406 Google Scholar
5. Krystal AD. Sleep and psychiatric disorders: future directions. Psychiatr Clin North Am. 2006;29(4):111530. Medline:17118285 Google Scholar
6. McCracken LM, Iverson GL. Disrupted sleep patterns and daily functioning in patients with chronic pain. Pain Res Manag. 2002;7(2):759. Medline:12185371 Google Scholar
7. Nagai MHS. Sleep duration as a risk factor for cardiovascular disease: a review of the recent literature. Curr Cardiol Rev. 2019;66(1):5461. Medline:21286279 Google Scholar
8. Spira AP, Chen-Edinboro LP, Wu MN, et al. Impact of sleep on the risk of cognitive decline and dementia. Curr Opin Psychiatry. 2014;27(6):47883. Medline:25188896 Google Scholar
9. Park J. A profile of the Canadian forces. Perspectives on Labour and Income. 2008;9(7). Available from: Google Scholar
10. Veterans Affairs Canada. Survey on transition to civilian life: report on regular force veterans [Internet]. Charlottetown (PE): Veterans Affairs Canada; 2011 [cited 2021 Mar 27]. Available from: Google Scholar
11. Peterson AL, Goodie JL, Satterfield WA, et al. Sleep disturbance during military deployment. Mil Med. 2008;173(3):2305. Medline:18419023 Google Scholar
12. Bray RM, Spira JL, Olmstead KR, et al. Behavioral and occupational fitness. Mil Med. 2010;175(Suppl 8):3956. Google Scholar
13. Thompson JM. A well-being construct for veterans’ policy, programming, and research. Veterans Affairs Canada Research Directorate Technical Report. Charlottetown (PE): Veterans Affairs Canada; 2016. Google Scholar
14. Hughes JM, Ulmer CS, Gierisch JM, et al. Insomnia in United States military veterans: an integrated theoretical model. Clin Psychol Rev. 2018;59:11825. Medline:29180102 Google Scholar
15. Troxel WM, Shih RA, Pedersen ER, et al. Sleep in the military: promoting healthy sleep among US service members. Rand Health Q. 2015;5(2):19. Medline:28083395 Google Scholar
16. Morin CM, Bélanger L, Bastien C, et al. Long-term outcome after discontinuation of benzodiazepines for insomnia: a survival analysis of relapse. Beh Res Ther. 2005;43(1):114. Medline:15531349 Google Scholar
17. Krueger GP. Sustained work, fatigue, sleep loss and performance: a review of the issues. Work Stress.1989;3(2):12941. Google Scholar
18. Lamarche LJ, De Koninck J. Sleep disturbances in adults with posttraumatic stress disorder: a review. J Clin Psychiatry. 2007;68(8):125770. Medline:17854251 Google Scholar
19. Schutte-Rodin SL, Broch L, Buysee D, et al. Clinical guidelines for the evaluation and management of chronic insomnia in adults. J Clin Sleep Med. 2008;4(5):487504. Medline:18853708 Google Scholar
20. Namikoshi T. Touch & stretch: Shiatsu for everyone. Tokyo: Japan Publications; 1985. Google Scholar
21. Honda Y, Tsuda A, Horiuchi S. Effect of a four-week self-administered acupressure intervention on perceived stress over the past month. Open J Med Psychol. 2012;1(3):204. Google Scholar
22. Makoto A. Shiatsu for women’s health. Aust Nurs Midwifery J. 2013;21(3):51. Medline:24279106 Google Scholar
23. Robinson N, Lorenc A, Liao X. The evidence for Shiatsu: a systematic review of Shiatsu and acupressure. BMC Complement Altern Med. 2011;11:88. Medline:21982157 Google Scholar
24. Beresford-Cooke C. Shiatsu theory and practice: a comprehensive text for the student and professional. 3rd ed. Toronto: Churchill Livingstone/Elsevier; 2011. Google Scholar
25. Chen ML, Lin LC, Wu SC, et al. The effectiveness of acupressure in improving the quality of sleep of institutionalized residents. J Gerontol A Biol Sci Med Sci. 1999;54(8):M38994. Medline:10496543 Google Scholar
26. Hsu W, Hsu HY, Sun JL. Effects of Shemen acupressure on improving the condition of institutional residents with insomnia. J Evid Based Nurs. 2006;2:3318. Google Scholar
27. Sun JL, Sung MS, Huang MY, et al. Effectiveness of acupressure for residents of long-term care facilities with insomnia: a randomized controlled trial. Int J Nurs Stud. 2010;47(7):798805. Medline:20056221 Google Scholar
28. Reza H, Kian N, Pouresmail Z, et al. The effect of acupressure on quality of sleep in Iranian elderly nursing home residents. Complement Ther Clin Pract. 2010;16(2):815. Medline:20347838 Google Scholar
29. Yuan SL, Matsutani LA, Marques AP. Effectiveness of different styles of massage therapy in fibromyalgia: a systematic review and meta-analysis. Man Ther. 2015;20(2):25764. Medline:25457196 Google Scholar
30. Yuan SL, Berssaneti AA, Marques AP. Effects of shiatsu in the management of fibromyalgia symptoms: a controlled pilot study. J Manipulative Physiol Ther. 2013;36(7):43643. Medline:23830713 Google Scholar
31. Long AF, Mackay HC. The effects of shiatsu: findings from a two-country exploratory study. J Altern Complement Med. 2003;9(4):53947. Medline:14499030 Google Scholar
32. Long AF. The effectiveness of shiatsu: findings from a cross-European, prospective observational study. J Altern Complement Med. 2008;14(8):92130. Medline:18990043 Google Scholar
33. Brown CA, Bostick G, Bellmore L, et al. Hand self-shiatsu for sleep problems in persons with chronic pain: a pilot study. J Integrative Med. 2014;12(2):94101. Medline:24666675 Google Scholar
34. Qin P, Dick B, Leung A, et al. Effectiveness of hand shiatsu to improve sleep following sport-related concussion in young athletes: a proof-of-concept study. J Integrative Med. 2019;17(1):249. Medline:30482473 Google Scholar
35. Styles E. The psychology of attention. New York: Psychology Press; 2006. Google Scholar
36. Harvey AG, Sharpley AL, Ree MJ, et al. An open trial of cognitive therapy for chronic insomnia. Behav Res Ther. 2007;45(10):2491501. Medline:17583673 Google Scholar
37. Fernandez E, Salem D, Swift JK, et al. Meta-analysis of dropout from cognitive behavioral therapy: magnitude, timing, and moderators. Consult Clin Psychol. 2015;83(6):110822. Medline:26302248 Google Scholar
38. Zollman FS, Cyborski C, Duraski SA. Actigraphy for assessment of sleep in traumatic brain injury: case series, review of the literature and proposed criteria for use. Brain Inj. 2010;24(5):74854. Medline:20334470 Google Scholar
39. Usui A, Ishizuka Y, Obinata I, et al. Validity of sleep log compared with actigraphic sleep-wake state II. Psychiatry Clin Neurosci. 1999;53(2):1834. Medline:10459683 Google Scholar
40. Cella D, Young S, Rothrock N, et al. The Patient-Reported Outcomes Measurement Information System (PROMIS): progress of an NIH roadmap cooperative group during its first two years. Med Care. 2007;45(5 Suppl 1):S3S11. Medline:17443116 Google Scholar
41. Buysse DJ, Reynolds CF, Monk TH, et al. The Pittsburgh Sleep Quality Index: a new instrument for psychiatric practice and research. Psychiatry Res. 1989;28(2):193213. Medline:2748771 Google Scholar
42. Gradisar M, Lack L, Richards H, et al. The Flinders Fatigue Scale: preliminary psychometric properties and clinical sensitivity of a new scale for measuring daytime fatigue associated with insomnia. J Clin Sleep Med. 2007;3(7):7228. Medline:18198807 Google Scholar
43. Adan ANA, Fabbri M, Natale V, et al. Sleep Beliefs Scale (SBS) and circadian typology. J Sleep Res. 2006;15(2):12532. Medline:16704566 Google Scholar
44. Hyland ME, Lewith GT, Westoby C. Developing a measure of attitudes: the holistic complementary and alternative medicine questionnaire. Complement Ther Med. 2003;11(1):338. Medline:12667973 Google Scholar
45. Institute of Medicine Committee on Sleep Medicine and Research; Colten HR, Altevogt BM, eds. Sleep disorders and sleep deprivation: an unmet public health problem. Washington (DC): National Academies Press; 2006. Google Scholar
46. Hughes JM, Song Y, Fung CH, et al. Measuring sleep in vulnerable older adults: a comparison of subjective and objective sleep measures. Clin Gerontol. 2018;41(2):14557. Medline:29283797 Google Scholar
47. Landry GJ, Best JR, Liu-Ambrose T. Measuring sleep quality in older adults: a comparison using subjective and objective methods. Front Aging Neurosci. 2015;7(7):166. Medline:26441633 Google Scholar
48. Zhang L, Zhao ZX. Objective and subjective measures for sleep disorders. Neurosci Bull. 2007;23(4):23640. Medline:17687399 Google Scholar
49. Unruh ML, Redline S, An MW, et al. Subjective and objective sleep quality and aging in the sleep heart health study. J American Geriatr Soc. 2008;56(7):121827. Medline:18482295 Google Scholar
50. Chang KJ, Son SJ, Lee Y, et al. Perceived sleep quality is associated with depression in a Korean elderly population. Arch Gerontol Geriat. 2014;59(2):46873. Medline:24852666 Google Scholar
51. Liu Y, Li T, Guo L, et al. The mediating role of sleep quality on the relationship between perceived stress and depression among the elderly in urban communities: a cross-sectional study. Public Health. 2017;149:217. Medline:28528223 Google Scholar
52. Press Y, Punchik B, Freud T. The association between subjectively impaired sleep and symptoms of depression and anxiety in a frail elderly population. Aging Clin Exp Res. 2018;30(7):75565. Medline:29022191 Google Scholar
53. Hughes JM, Ulmer CS, Hastings SN, et al. Sleep, resilience, and psychological distress in United States military Veterans. Mil Psychol. 2018;30(5):40414. Google Scholar
54. Grah SC, Dzierzewski JM, Ravyts SG, et al. General and domain-specific self-efficacy and sleep in older adults. J Sleep and Sleep Disorders. 2017;40(Suppl 1): A315. Google Scholar

The authors have nothing to disclose.

Cary A. Brown, Annette Rivard, and Leisa Bellmore conceived and designed the research and contributed to the manuscript. Rivard carried out the data analysis. Morgan Kane and Yuluan Wang conducted the data collection and contributed to the manuscript. All authors approved the final version submitted for publication.

The study protocol was approved by the Research Ethics Board at the University of Alberta, Edmonton, Alberta, Canada.

All participants provided written informed consent.

RSO University of Alberta: Pro00086318.


The authors gratefully acknowledge the funding support for this study provided by the Veteran and Family Well-Being Fund, Veterans Priority Programs Secretariat, Veterans Affairs Canada/Government of Canada.

This manuscript has been peer reviewed.


The authors gratefully acknowledge the generous contribution of time from the study participants and Suzette Bremault-Phillips and Bruce Dick for input on the original study proposal.