PMCCPMCCPMCC

Search tips
Search criteria 

Advanced

 
Logo of nihpaAbout Author manuscriptsSubmit a manuscriptHHS Public Access; Author Manuscript; Accepted for publication in peer reviewed journal;
 
J Am Geriatr Soc. Author manuscript; available in PMC 2017 April 1.
Published in final edited form as:
PMCID: PMC4843834
NIHMSID: NIHMS748962

Wrist Accelerometry in the Health, Functional, and Social Assessment of Older Adults

To the Editor

The importance of accelerometry as an indicator of older adult health is increasingly recognized. Hip accelerometry is associated with disability, cardiovascular risk, and poor health outcomes and is considered a more precise measure of activity and sedentary behavior than wrist.1 However, wrist accelerometers have become ubiquitous commercially and are being used increasingly in research as a result of new activity monitor protocols (e.g, National Health and Nutrition Examination Study). Currently, we lack data relating wrist accelerometry to older adult health. In this analysis, we associate wrist accelerometry with an extensive set of health outcomes essential to older adults’ well-being using a nationally-representative community sample.

METHODS

We analyzed data from the wrist accelerometry sub-study (n=738) in Wave 2 of the National Social Life, Health, and Aging Project (NSHAP).2 The ActiWatch® Spectrum (Philips Respironics3), worn on the non-dominant wrist, continuously measured activity and sleep over 72 hours (not removed for water activities).4 Activity “counts” were recorded every 15 seconds (epoch). A galvanic sensor excluded non-wear time. Wake time was determined using Actiware® software protocols and investigator adjustment to align event markers, ambient light, and activity data.5 Only days with ≥10 hours of wake time were included. Average daily activity was calculated as the sum of the wake time counts divided by the total number of epochs. Valid hours worn, number of weekend days worn (categorical, reference: no weekend days), and month of wear (categorical, reference: January) to approximate season were calculated.

Outcomes

Self-rated physical and mental health (excellent, very good, good, fair, poor) were assessed. Systolic and diastolic blood pressure, non-fasting glycosylated hemoglobin (HbA1C, %), C-reactive protein (CRP, mg/L) (blood spots), and body mass index (BMI, kg/m2) were measured.2 Obesity was identified as BMI ≥30 kg/m2. Study participants reported any diagnoses of heart problems, diabetes, stroke, cancer (other than skin), or asthma/chronic obstructive pulmonary disease (COPD)/emphysema.6 Respondents performed a 3-meter timed walk twice and 5 timed serial chair stands.4 The fastest walk and chair stands times were recorded. Difficulty performing any activity of daily living (ADL) or instrumental activity of daily living (IADL) was self-reported.4 Frequency of 11 depressive symptoms and 7 anxiety symptoms “during the past week,”7 frequency of attending meetings of organized groups and socializing with friends or family at least once per month, and current alcohol or tobacco use were self-reported.

Covariates

Age at the time of survey; gender; education; race; Hispanic ethnicity; household assets; and current working status were self-reported. Cognitive function was determined using the survey-adapted Montreal Cognitive Assessment (MoCA-SA).8

Statistical Analysis

Association of average daily activity with each outcome was assessed using survey-adjusted regression models controlling for covariates, wear time, weekend days, and wear month. Effects per 10 activity counts are reported. Analyses were conducted using Stata 14 (NSHAP data release v2.2:d34186f4ce5f).

RESULTS

Of the 738 participants, 631 had complete accelerometer and covariate data. Age ranged from 71.2–72.4 years, 52.8% were female, and 83.6% were White/Caucasian. The average daytime activity count was 54.2 (95% CI: 52.1–56.4), mean number of valid wake hours was 36.4 (95% CI: 35.4–37.3), and 59.6% of participants did not wear the accelerometer on a weekend day (95% CI: 55.6%–63.4%).

Multivariate regression models demonstrated that all accelerometry-health outcome relationships were in the expected direction. Higher physical activity was significantly associated with better reported physical (p<0.001) and mental health (p=0.009), lower DBP (p=0.01), lower BMI (p<0.001), lower CRP (p=0.02), less obesity (p<0.001), lower HgbA1c (p=0.01), less reported heart problems (p=0.01) and diabetes (p< 0.001), faster 3-meter walk (p< 0.001), faster chair stands (p=0.002), and less reported ADL (p=0.002) and IADL difficulty (p=0.006). For example, a 10 point increase in mean activity count was associated with a 0.98 (=exp(−0.02)) seconds faster walk, and a 20% lower risk of reporting an ADL disability.

DISCUSSION

Similar to hip devices, wrist accelerometry-measured average daily activity was significantly associated with many physical and functional older adult health outcomes but with few mental health and social engagement outcomes. To our knowledge, this is the first report showing significant associations between wrist accelerometry and a wide range of health outcomes in a nationally-representative older adult sample.9 In this study, accelerometry was assessed over three days, and our findings of strong associations with this brief wear duration suggest that accelerometry may be feasible in clinic settings. Longer durations (e.g., ≥5 days10) may more fully represent habitual activity, increase reliability and therefore strengthen the accelerometry associations found with health outcomes. With newer wrist devices and algorithms for interpreting output, ease of wear, and low battery requirements, wrist accelerometers may become clinically useful moving forward.

TABLE
WRIST ACCELEROMETRY AND OLDER ADULT HEALTH OUTCOMES (N=631)

Acknowledgments

Source of Funding: This work was supported by the National Institute on Aging (1R01AG033903-01, R01 AG030481-01A1, R37 AG030481).

Sponsor’s Role: None

Footnotes

Conflict of Interest: The editor in chief has reviewed the conflict of interest checklist provided by the authors and has determined that the authors have no financial or any other kind of personal conflicts with this paper.

Authors’ Contributions.

Huisingh-Scheetz M – concept, design, analysis, interpretation, drafting, revising/writing, approval of final draft.

Kocherginsky M – concept, design, analysis, revising, approval of final draft.

Dugas L – interpretation, revising, approval of final draft.

Payne C – acquisition of data, revising, approval of final draft.

Dale W – conception, revising, approval of final draft.

Conroy DE - conception, revising, approval of final draft.

Waite L – conception, design, interpretation of data, revising, approval of final draft.

References

1. Rosenberger ME, Haskell WL, Albinali F, et al. Estimating activity and sedentary behavior from an accelerometer on the hip or wrist. Med Sci Sports Exerc. 2013;45:964–975. [PMC free article] [PubMed]
2. O’Doherty K, Jaszczak A, Hoffmann JN, et al. Survey Field Methods for Expanded Biospecimen and Biomeasure Collection in NSHAP Wave 2. J Gerontol B Psychol Sci Soc Sci. 2014;69(Suppl 2):S27–37. [PMC free article] [PubMed]
3. Philips Respironics. Actiwatch. Koninklijke Philips Electronics N.V; 2013.
4. Huisingh-Scheetz M, Kocherginsky M, Schumm PL, et al. Geriatric Syndromes and Functional Status in NSHAP: Rationale, Measurement, and Preliminary Findings. J Gerontol B Psychol Sci Soc Sci. 2014;69(Suppl 2):S177–190. [PMC free article] [PubMed]
5. Lauderdale DS, Philip Schumm L, Kurina LM, et al. Assessment of sleep in the national social life, health, and aging project. J Gerontol B Psychol Sci Soc Sci. 2014;69(Suppl 2):S125–133. [PMC free article] [PubMed]
6. Vasilopoulos T, Kotwal A, Huisingh-Scheetz MJ, et al. Comorbidity and Chronic Conditions in the National Social Life, Health and Aging Project (NSHAP), Wave 2. J Gerontol B Psychol Sci Soc Sci. 2014;69(Suppl 2):S154–165. [PMC free article] [PubMed]
7. Payne C, Hedberg EC, Kozloski M, et al. Using and interpreting mental health measures in the national social life, health, and aging project. J Gerontol B Psychol Sci Soc Sci. 2014;69(Suppl 2):S99–S116. [PMC free article] [PubMed]
8. Kotwal AA, Schumm P, Kern DW, et al. Evaluation of a Brief Survey Instrument for Assessing Subtle Differences in Cognitive Function Among Older Adults. Alzheimer Dis Assoc Disord. 2015;29:317–324. [PMC free article] [PubMed]
9. Huisingh-Scheetz M, Kocherginsky M, Magett E, et al. Relating wrist accelerometry measures to disability in older adults. Arch Gerontol Geriatr. 2016;62:68–74. [PMC free article] [PubMed]
10. Hart TL, Swartz AM, Cashin SE, et al. How many days of monitoring predict physical activity and sedentary behaviour in older adults? Int J Behav Nutr Phys Act. 2011;8:62. [PMC free article] [PubMed]