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Optimal Design of Experiments: A Case Study Approach by Peter Goos and Bradley Jones

2012, International Statistical Review

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Industrial Design of Experiments

A Case Study Approach for Design and Process Optimization

• © 2022
• Sammy Shina 0

Department of Mechanical Engineering, University of Massachusetts Lowell, Framingham, USA

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• Presents classical versus Taguchi DoE methodologies as well as techniques developed by the author for successful DoE
• Offers a step-wise approach to DoE optimization and interpretation of results
• Includes industrial case studies, worked examples and detailed solutions to problems.

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Table of contents (10 chapters)

Front matter, data presentations, statistical distributions, quality tools, and relationship to doe.

Sammy Shina

Samples and Populations: Statistical Tests for Significance of Mean and Variability

Regression, treatments, doe design, and modelling tools, two-level factorial design and analysis techniques, three-level factorial design and analysis techniques, doe error handling, significance, and goal setting, doe reduction using confounding and professional experience, multiple-level factorial design and doe sequencing techniques, variability reduction techniques and combining with mean analysis, strategies for multiple outcome analysis and summary of doe case studies and techniques, back matter.

• Analysis of Experiments
• Optimization
• Taguchi Method
• Saturated design
• Factorial design
• Multiple factor and level design
• Statistical analysis
• Variability reduction
• Multiple regression
• Design of Experiments
• Variability analysis

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Book Title : Industrial Design of Experiments

Book Subtitle : A Case Study Approach for Design and Process Optimization

Authors : Sammy Shina

DOI : https://doi.org/10.1007/978-3-030-86267-1

Publisher : Springer Cham

eBook Packages : Engineering , Engineering (R0)

Copyright Information : The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022

Hardcover ISBN : 978-3-030-86266-4 Published: 04 January 2022

Softcover ISBN : 978-3-030-86269-5 Published: 05 January 2023

eBook ISBN : 978-3-030-86267-1 Published: 03 January 2022

Edition Number : 1

Number of Pages : XXXI, 368

Number of Illustrations : 62 b/w illustrations, 24 illustrations in colour

Topics : Engineering Design , Statistical Theory and Methods , Industrial Design , Industrial and Production Engineering , Business and Management, general

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From David E. Gray \(2014\). Doing Research in the Real World \(3rd ed.\) London, UK: Sage.

• Study Protocol
• Open access
• Published: 11 June 2024

Neuromuscular electrical stimulation to combat cognitive aging in people with spinal cord injury: protocol for a single case experimental design study

• Wouter A. J. Vints 1 , 2 , 3 ,
• Oron Levin 1 , 4 ,
• Martijn van Griensven 5 ,
• Johan W. S. Vlaeyen 6 , 7 ,
• Nerijus Masiulis 1 , 8 ,
• Jeanine Verbunt 2 , 3 &
• Charlotte C. M. van Laake-Geelen 2 , 3  

BMC Neurology volume  24 , Article number:  197 ( 2024 ) Cite this article

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Introduction

Individuals with spinal cord injury (SCI) can experience accelerated cognitive aging. Myokines (factors released from muscle cells during contractions), such as brain-derived neurotrophic factor (BDNF), are thought to have beneficial effects on cognition. Neuromuscular electrical stimulation (NMES) was shown to elicit a large release of myokines. However, the effects of NMES on cognitive function have not been studied.

To present the study protocol for a clinical trial evaluating the effects of NMES aimed at improving cognition and BDNF.

A replicated randomized three-phases single-case experimental design (SCED) with sequential multiple baseline time series and a single-armed prospective trial will be conducted with 15 adults with chronic SCI (> 12 months after injury) above L1 neurological level undergoing 30-min quadriceps NMES, 3 days per week for 12 weeks.

Main study endpoints

Primary endpoint is cognitive performance (assessed by a smartphone test) conducted three times per week during the baseline phase with random duration of 3 to 8 weeks, the intervention phase of 12 weeks, and the follow-up phase of 3 weeks after a no measurement rest period of 12 weeks. Secondary endpoints are changes in BDNF levels and cognitive performance measured before the baseline period, before and after intervention and after a 12 weeks follow-up.

This will be the first study investigating the effects of 12 weeks NMES on both cognition and BDNF levels in individuals with SCI. The SCED results provide information on individual treatment effect courses which may direct future research.

Trial registration

ClinicalTrials.gov (NCT05822297, 12/01/2023).

Peer Review reports

Cognitive problems in persons with spinal cord injury (SCI) have long been underrecognized and were usually explained by concomitant traumatic brain injury (TBI). However, since less than five years, it has been noted that individuals with SCI suffer from accelerated cognitive aging, even when correcting for mood factors and in the absence of TBI [ 1 , 2 , 3 , 4 ]. Individuals with SCI face a 13-fold increase in the risk of cognitive impairment compared to the general population [ 5 ] and they are twice as likely to develop Alzheimer’s dementia [ 6 , 7 , 8 ]. The cognitive domains most affected following SCI are executive functions, long-term memory, short-term memory, attention [ 2 ], processing speed and verbal fluency [ 1 ] when controlling for mood factors. This cognitive impairment begins in the subacute phase and seems to worsen over time [ 9 ]. It was found that individuals with SCI have cognitive impairments and brain activation patterns that are similar to healthy adults that are on average 20 years older [ 1 , 10 ]. A proposed mechanism for this cognitive decline is neuroinflammation [ 4 , 11 ]. Neuroinflammation plays a role in normal age-related cognitive decline [ 12 , 13 , 14 ] and following spinal injury, chronic elevation of neuroinflammation is found in the whole central nervous system [ 15 , 16 , 17 ].

Physical exercise interventions have been shown to have anti-inflammatory effects, induce an elevation of neurotrophic factors in the bloodstream, potentiate neuroplastic processes in the brain, increase brain volume and benefit cognitive function [ 18 , 19 , 20 , 21 ]. The anti-inflammatory effect and increase in neurotrophic factors during exercise results in part from the release of products from contracting muscle cells (i.e. myokines) [ 20 ]. This release of myokines has been reported following all sorts of muscle training, including electrical stimulation [ 20 , 22 ]. It is often difficult for persons with SCI to sufficiently engage in physical exercise, with only 50% of SCI patients engaging in any leisure-time physical activity at all [ 23 ]. This difficulty may depend on the level of injury and the severity of motor function loss, as well as the availability of sport opportunities for wheelchair users. Electrical stimulation has been proposed as an interesting addition or alternative for increasing circulating myokine levels in humans with difficulty to participate in regular exercise programs [ 22 ]. Electrical stimulation can be used alone, i.e. neuromuscular electrical stimulation (NMES), or to assist voluntary exercise, i.e. functional electrical stimulation (FES). NMES induces contraction of myocytes similarly to exercise, which results in the release of myokines in the circulation, as recently reviewed by Sanchis-Gomar et al. (2019) [ 22 ]. A widely studied myokine is brain-derived-neurotrophic factor (BDNF), which is released from neurons [ 24 ], astrocytes [ 25 ], platelets [ 26 ], and skeletal muscle [ 27 ] and can trigger neuroprotective and neurotrophic effects [ 28 ]. Some studies suggest that most of the circulating BDNF is released from the brain [ 29 ]. Some other myokines, like insulin-like growth factor-1 and irisin were suggested to induce the release of brain-derived BDNF [ 20 ]. Remarkably, one study in young healthy adults reported that circulating levels of the myokine named BDNF increased more following a single bout of NMES than voluntary exercise with the same integrated force of muscle contraction [ 30 ]. Similarly, in a rat study, assessing able-bodied rats, the increase in BDNF levels was two times higher after four weeks of NMES compared to four weeks of running [ 31 ]. Also in human studies, long-term interventions with NMES were reported to increase circulating levels of BDNF, for example in older adults with type 2 diabetes [ 32 ]. BDNF can be considered one of the most important exercise-induced factors as it has direct beneficial effects on synaptic plasticity and neurogenesis, and it was linked with brain atrophy and cognitive function in a wide array of research [ 33 ]. So far, however, we were unable to find any studies reporting a beneficial effect of NMES on cognitive function. To the best of our knowledge, only one other study has evaluated the effects of FES [ 34 ] and only one study evaluated the effects of NMES [ 35 ] on cognitive function. The FES study is also the only study investigating exercise effects on cognition in SCI patients. In this study, improvements on a working memory task were reported after 6 months of functional electrical stimulation (FES)-assisted rowing exercise in SCI patients. This study did not measure myokine levels [ 34 ]. The authors of the NMES study evaluated the effect of a short intervention of 15 days of NMES in coronary bypass patients and discovered that patients in the experimental group showed increases in functional connectivity in the brain frontoparietal, salience and sensorimotor networks [ 35 ]. Overall, there is a lack of knowledge on the effects of electrical stimulation to improve cognitive function and the dose–response needed to attain such effect.

We will use a randomized replicated ABC single case experimental design (SCED) [ 36 ]. Primarily, we will examine to what extent and after how many weeks a 12 week intervention with NMES may change performance of people with SCI on the momentary digital symbol substitution task, an information processing task that will repeatedly be administered via a smartphone application. NMES will be applied to the quadriceps muscles of individuals with SCI. An intervention of 12 weeks is considered to be sufficient to induce both a myokine response and muscular changes. Secondarily, we will assess changes in the myokine BDNF before and after the 12 week intervention with NMES and after a 12 week follow-up without NMES. We hypothesize that NMES will have a beneficial effect on cognitive performance starting from the first weeks and that 12 weeks of NMES will induce an elevation of BDNF levels. This study tests a new treatment strategy, accessible even to individuals with low physical exercise possibilities, with the potential to slow down or prevent further cognitive decline in persons with SCI. The explorative findings may guide future research. Furthermore, it may help to raise awareness of the process of accelerated cognitive decline in persons with SCI.

The Standard Protocol Items: Recommendations for Interventional Trials (SPIRIT) guidelines (see Appendix) and RoBiNT scale for risk of bias in SCED studies were used in designing and describing this clinical trial [ 37 , 38 ].

The trial was registered with ClinicalTrials.gov (NCT05822297) on the 12th of January 2023, sponsored by Adelante Zorggroep and approved by the Medical Ethical Testing Committee (reference number W23.071) of Maxima Medical Center at Veldhoven, the Netherlands on the 9th of October 2023. In case of important protocol modifications, they will be notified to the Medical Ethical Testing Committee and updated in the trial registry.

Participants

Participants and recruitment: Participants will be recruited from the outpatient clinic of the rehabilitation center Adelante Centre of Expertise in Rehabilitation and Audiology, locations Hoensbroek and Maastricht University Medical Center (MUMC +)., the Netherlands. The rehabilitation physician will inform potential participants about the study and flyers with information will be handed over. The flyers will include the contact details of one of the researchers. Alternatively, participants can give permission to the physicians that their contact information can be sent to the researchers.

Inclusion criteria: Participants are eligible if they are 18 years or older, have SCI since at least one year (chronic phase), the injury level is L2 or higher (meaning that the quadriceps muscle is likely affected to some extent, since this muscle is innervated by radicular nerves L3-L4), completeness of injury has been scored according to the American Spinal Injury Association (ASIA) Impairment Scale (AIS) [ 39 ] as either A, B or C, if they are able to use apps on their smartphones, if they are able to use NMES device safely at home, and if they speak Dutch at a native level. We will exclude participants if we cannot induce visible or palpable contractions of the quadriceps muscles with NMES or if they cannot tolerate the NMES intervention. We will exclude participants with a diagnosis of cancer, neurodegenerative or psychiatric disorders, current pressure ulcers, a history of severe autonomic dysreflexia (systolic blood pressure elevations above 200 mmHg), have metal implants in the stimulation site, an intrathecal baclofen pump, or are currently pregnant. Finally, we exclude participants who have taken part in a program with electrical stimulation in the last 6 months prior to the study.

Study design and procedures

Study design.

This study will apply a randomized replicated sequential ABC single-case experimental design (SCED), with a baseline (A), intervention (B) and follow-up (C) phase [ 36 , 40 ]. SCED study designs demonstrate strong internal validity to determine the likelihood of a causal relationship between the intervention and outcomes. One entity is observed repeatedly over a certain time period under different levels of at least one independent variable. The power in SCEDs is related to the number of data points for each participant and not the number of participants. Each participant serves as his/her own comparison, thus controlling for confounding variables that can impact outcome and allowing heterogeneous clinical presentations. The internal validity of the SCED results can be improved by randomization techniques such as randomization of the starting time of the intervention. The downside is that the generalizability of the study results to the total population still depends on the number of participants included (i.e. replication of the findings) [ 40 , 41 ]. The SCED lend itself perfectly to reflect on the reasons for the (likely) variability in the onset of NMES-induced effects on cognitive performance, looking at individual participant’s intervention courses. Thus, allowing to reflect on the existence of different potentially moderating characteristics. Several potential moderators have been suggested previously [ 4 ] and will be measured within this study. These include concomitant traumatic brain injury, psychological alterations, chronic pain, hemodynamic dysregulation, level of injury, respiratory failure, substance (ab)use, educational level, cardiovascular diseases, and intensive care admission [ 4 ]. In addition, a SCED design allows us to interpret after how many weeks the NMES leads to changes in performance on the momentary digital symbol substitution task. The aforementioned advantages of SCED studies indicate how this design offers additional information that is well fitted for an explorative study in a new topic.

In the context of SCED studies, randomization does not refer to individuals being randomly allocated to treatment groups, but to the random onset of the intervention phase which is usually set within a fixed window of time in order to make this randomization more feasible [ 38 ]. In this study, a window of 9 to 24 measurement points (i.e. 3 to 8 weeks at the set frequency of three test assessments per week) will be employed in which the change of phase from baseline to intervention phase will be randomly determined. This determination will be done with a random number generator in Excel set to give a random number between 9 and 24. There will be a baseline phase ranging from 3 to 8 weeks, followed by an intervention phase of 12 weeks and a final follow-up phase of 3 weeks after a 12 week rest period with no measurements and no interventions. As we will exclude the first 4 measurements in the baseline phase from analysis, see Sect. " Statistical analysis ", this leaves us at least 5 measurement points in this phase, 36 measurement points in the intervention phase and 9 measurement points in the follow-up phase. Neither participants nor researchers will be blinded to the phase of the intervention. Replication of the experiment will be done in 15 participants.

Finally, additional measurements will be done in a repeated manner according to a single-armed prospective design before baseline phase, at the end of the baseline phase, at the end of the intervention phase and at the start of the follow-up phase.

Study procedures

The experimental design is illustrated in Fig.  1 . Upon the first appointment (T1), participants will sign an informed consent and undergo baseline examinations consisting of questionnaires and clinical tests. A smartphone application for repeated monitoring of cognitive function will be installed on their smartphones and they will be familiarized with the cognitive tests and neuromuscular electrical stimulation intervention. On a separate day (T2), within seven days from the first appointment, they will undergo a series of cognitive tests and venous blood samples will be collected. Furthermore, they will fill in a second list of questionnaires and undergo a clinical evaluation of strength and spasticity. This will be followed by a no-treatment baseline period with a random length of 3–8 weeks. Upon the third (T3), fourth (T4) and fifth (T5) visit, the oral cognitive tests, venous blood sample collection and questionnaires from T2 will be repeated. During the third visit (T3) the participants will receive their own NMES device for home-based training and they will be asked to return it 12 weeks later on the fourth visit (T4). All participants will undergo the same intervention. During the fourth visit (T4) the clinical evaluation of strength and spasticity that was done at T2 will be repeated. Between T2 and T4 and between T5 and T6 participants will undergo smartphone cognitive tests 3 times per week. At the end of the study, no additional tests will be done. Participants will be requested to write in a diary the specifications of the NMES session (duration, intensity) after each session and indicate when they performed a cognitive test. They can also write down any problems they encountered using the NMES device or smartphone application. Participants receive a telephone call once a week by one of the researchers to assess how they feel, be reminded of the diary and to assure there are no technical issues with the NMES device or smartphone application. Participants are allowed to withdraw from the study at any time. Available data from these participants will be included in the data analyses whenever possible.

Experimental design. T1-5 represent measurement time points. During T2-5 the same measurements are repeated, expect the addition of clinical examination in time point T4. The red line is the expected change in cognitive test performance on the smartphone-based cognitive test without intervention, which we expect to show a fast increase in the first ± 3 sessions followed by a slight increase over time due to a learning effect. The X’s mark how cognitive test results are hypothesized to change due to the intervention

Intervention

The intervention consists of 12 weeks NMES which will be done using the Pierensymphony M, serie A, article number 104800 (10,003,566) (manufactured by Pierenkemper GmbH and distributed by schwa-medico Nederland B.V.), a two-channel NMES device designed, and CE marked for muscular electrical stimulation (CE 0482). After a familiarization session, the intervention will be home-based, three times a week, with at least 1 day between stimulation sessions and 2 days between a stimulation session and testing day. Electrical stimulation will be done on the quadriceps muscles of both legs simultaneously. For each leg, one electrode is placed on the proximal lateral side and one on the distal medial side of the quadriceps muscle (see Fig.  2 ). Electrical stimulation will take 30 min, at a stimulation frequency of 50 Hz, and a pulse width of 400 μs. We will choose the highest intensity that is easily supported by the participant without inducing discomfort with a maximum intensity of 100 mA. We should at least see a visible or palpable contraction or the participant will be excluded. The activation within the activation-rest cycle consists of a 1 s ramp-up, 7 s full activation and 1 s ramp-down, followed by 18 s rest. Every 4 weeks the rest period will be diminished with 3 s until the rest phase is equal to the activation phase (9 s). Participants may continue their normal treatments, which may consist of physiotherapy. However, they will be asked not to change their physical activity habits during the experiment.

Placement of the electrodes on the quadriceps muscles

Cognitive assessments

Smartphone-based cognitive assessment.

To study the primary aim of our study, participants will undergo a cognitive test for 3 times a week during the baseline, intervention and follow-up phase of the experiment. This repetitive cognitive test will be administered using a secured smartphone application, which was designed for use in clinical settings and research (m-Path, https://m-path.io/landing/ ). It will be programmed to emit an auditory signal three times a week signaling the availability of a new cognitive test between 7.30 AM and 10.30 PM on the days that participants do not undergo NMES sessions. The test is the momentary digital symbol substitution task, which is a measure of processing speed and short-term working memory function. For this test, participants have 30 s time to complete as many trials as possible where they need to correctly select the figure representing the number given as depicted in the legend provided on the top of the screen. The outcome is presented as response time derived from the number of trials/30 s (speed, in ms) and the percentage of correct trials (accuracy). Before the test starts, participants will be informed that they should be ready to respond as fast and accurate as possible. Daniëls et al. (2020) have validated this test for repeated use in healthy adults. They reported a significant learning effect for number of correct answers in 30 s between the first and 48th trial (B = 0.32, SE = 0.04, p  < 0.001; with number of correct answers as the dependent variable and the logarithmic transformation of the session number (between 1 and 48) as the independent variable) [ 42 ].

Oral cognitive test battery

At four time points, participants will undergo an oral cognitive test battery consisting of seven tests that were validated for repeated use in people with SCI, even with impaired hand function [ 43 ]. The Dutch version of these tests will be used, whenever the test depends on language. The tests have a good-to-excellent test–retest reliability, except the RAVTL recognition score which has been shown to have a poor test–retest reliability [ 43 ]. This test was still used as the interference recall and delayed recall scores of this test have a good test–retest reliability in people with SCI [ 43 ]. To further decrease learning effects of repeated administration of the cognitive tests, different versions will be used on different visits if available. The test versions will be numbered and on their first visit participants will be asked to draw a random number from an envelope including a number for every existing version of the cognitive test, until the envelope is empty. This is repeated for every cognitive test. The sequence will be reported for the next visits. The cognitive tests are presented in Table  1 .

Blood analysis

Venous blood samples will be taken at the antecubital vein. Blood samples will be collected in two 5 mL serum separator tubes. After blood collection, the tubes will be gently inverted 6–8 times and within 90 min be centrifuged. Quantitative determination of serum BDNF will be measured using enzyme-linked immunosorbent assays (ELISA). Blood will be drawn four times (T2-5) per participant during the whole study project. To decrease errors due to ELISA kit differences, we will store the blood samples in a refrigerator compartment at the Maastricht University laboratory site at -80 °C until analysis, using the same kit for all blood measurements of the same participant at the end of the project.

Demographic and clinical assessments

At baseline, we will collect information on participant’s age, gender, smoking status, years of education, cause of the SCI, date of the SCI, drug/alcohol use, body mass index (BMI), medical history such as diabetes mellitus or hemodynamic regulation problems (hypertension, hypotension, autonomic dysreflexia) or respiratory problems (nocturnal apnea or use of a device for sleep apnea, signs of restricted or obstructive lung disease on spirometry test) or history of intensive care admission, medication use, history of TBI, and infections or wounds in the past 3 weeks. Some clinical tests will also be conducted upon baseline, while others are also measured at follow-up since we expect they may change due to our intervention. The latter are therefore seen as secondary outcomes and may be evaluated in a separate study.

The following clinical tests will be conducted:

At measurement timepoint 1

SCI classification: Functional impairment in terms of sensation and strength of key muscles will be assessed using the AIS classification [ 39 ]. It is a structured clinical assessment of the level and completeness of the spinal cord injury. Intra- and interobserver correlation coefficients are generally around 0.9 [ 44 ].

Lung spirometry: A portable spirometer (Vitalograph In2itive) will be used to assess forced expiratory volume in one second (FEV1), forced vital capacity (FVC) and peak cough flow (PCF) (L/s).

Heart rate and blood pressure measurement using an automated device will be measured at the left or right arm in seated position.

At measurement timepoint 1 and 4

Spasticity level: The Perceived Resistance to Passive Movement (PRPM) test is recommended by the Dutch guidelines for measuring spinal spasticity [ 45 ]. The scale ranges from (0) ‘no increased resistance’ to (4) ‘movement of the limb impossible’. Resistance of elbow flexors, elbow extensors, wrist flexors, wrist extensors, hip adductors, knee flexors, knee extensors, ankle plantar flexors (both with extended and with a 90° flexed knee) will be assessed in supine position. A sum of the scores will be used as the outcome measure, ranging from 0 to 32.

Electrically-evoked muscle strength: The MicroFET2 (Hoggan Scientific, LLC) muscle strength testing system is an handheld dynamometer, which will be used to measure electrical stimulation-evoked quadriceps force. The electrical stimulation protocol will be equal to that used during the 12 week intervention. The dynamometer will be affixed to a band, which in turn will be secured around the wheelchair and the distal part of the participant's lower leg. The MicroFET2 dynamometer demonstrates a high intra-rater reliability for knee extension, with an interclass correlation coefficient (ICC) of 0.93, a standard error of measurement (SEM) of 17.2N and a minimal detectable change of 47.5N [ 46 ].

The following questionnaires will be used:

Brief Physical Activity Assessment Tool: This tool is a two question physical assessment. It has a validity similar to that of more detailed self-report measures of physical activity. It can be used efficiently in routine primary healthcare services to identify insufficiently active patients who may need physical activity advice [ 47 ].

TBI-4 questionnaire: This questionnaire is specifically designed as a self-report tool to determine the likelihood of TBI in traumatic SCI patients utilizing just two questions. The outcome is presented as improbable TBI, possible TBI, mild TBI, moderate TBI or severe TBI. At the cut-off “possible TBI” this tool had a sensitivity of 83% and a specificity of 51% to detect mild TBI based on medical reports, indicating this questionnaire can be used as a guide in the unavailability of medical reports, but should be interpreted with care [ 48 ].

At measurement timepoints 2 till 5

Cognitive Failures Index: This test can reliably evaluate how participants experience their own cognitive function. It consists of 25 questions, each scored on a 5-point scale ranging from never (0) to very often (5) [ 49 ].

McGill Pain Questionnaire: Participants will be given a figure of a person, where they can color the zone of pain. They will be asked to describe the pain and score it on a 10 point visual analogue scale (VAS) and will receive some questions related to the impact of pain on their daily life [ 50 ].

Hospital Anxiety and Depression Scale (HADS): this scale is a widely used and reliable screening instrument to assess the severity of anxiety and depression [ 51 ]. A subtest score of more than 8 on 21 denotes considerable symptoms of anxiety or depression.

Fatigue Severity Scale: This questionnaire encompasses nine questions and has previously been used to recognize and diagnose fatigue in patients with neurological disorders [ 52 ].

Pittsburgh Sleep Quality Index: This index consists of 19 questions concerning seven domains related to sleep quality in the preceding month. Every domain receives a score of 0–3, with a total score of 0–21. A score of more than 5 indicates a poor sleep quality [ 53 ].

Utrecht Scale for Evaluation of Rehabilitation – Participation (USER-P): This test is widely used to assess participation in daily life activities in rehabilitation contexts. It contains 32 questions related to the frequency of participation, participation restrictions and satisfaction with participation, with a total score of 0–100 for each of these three domains. A higher score indicates a better participation [ 54 ].

The baseline measurements of potential moderators of the effect of our intervention on cognitive and blood outcomes and risk factors for cognitive impairments according to a previous review paper will be entered in the statistical analysis [ 4 ]. The risk factors for cognitive impairments will be entered as a combined risk score with a maximum score of 10, indicating the highest risk. Participants receive a score between zero and one for each of the following: 1 point if self-reported history of TBI or mild to severe concomitant TBI according to TBI4 questionnaire [ 48 ], 0.5 points for a score of > 8/21 for anxiety and 0.5 points for a score of > 8/21 for depression on the HADS scale [ 51 ], 1 point for chronic pain indicated on the McGill pain questionnaire to exist longer than 3 months and is scored at its minimum a VAS score of 3 [ 50 ], 0.5 points for a history of autonomic dysreflexia and 0.5 points for a blood pressure below 90/60 upon measurement, 1 point for tetraplegic participants compared to 0 points for paraplegic participants, 0.33 points for self-reported sleep apnea, 0.33 points for a FVC value below 0.85% of the predicted value, and 0.33 points for a PCF below 4.5L/s, 0.25 points for regular alcohol use (more than once per week), 0.25 points for drug abuse, 0.25 points for polypharmacy (more than 7 prescribed drugs), 0.25 points for any medication acting on the central nervous system, 1 point for participants who finished only basic education, 0.5 points for participants who finished only secondary education, 0.5 points for diabetes mellitus, 0.5 points for a BMI equal to or above 25 kg/m 2 [ 55 ], 1 point for previous intensive care admission.

Statistical analysis

Single-case data.

For our primary aim, the smartphone cognitive assessment, statistical analysis will be performed using the Shiny app for Single-Case Data Analysis (Shiny SCDA) [ 56 ]. A total of 54–69 measurements per participant will be attained.

Visual analysis

First, measurement points will be plotted and visually inspected per participant. Six features will be visually examined according to the guidelines of Lane and Gast (2014): cognitive performance in the different phases, variability in cognitive performance both within and between phases, trends in the data, immediacy of effect, overlap of data points between phases, and consistency of data patterns across participants [ 57 ]. Instead of inspecting only immediate effects, also the existence of a potential delay of the effect and consistency of this delay between participants will be explored [ 58 ].

Effect size measures

Finally, effect size measures will be calculated for each participant. This will be done by calculating the percentage of non-overlapping pairs between phases [ 59 ]. The NAP (Nonoverlap of All Pairs) value equals the number of comparison pairs showing no overlap, divided by the total number of comparisons, and can be considered an area under the curve percentage from a receiver operating characteristic analysis [ 60 ].

Randomization test

Subsequently, randomization tests will be performed to test the null hypothesis that NMES does not have an effect on participant’s cognitive function. The observations of the baseline phase will be compared to those of the intervention and follow-up phases respectively. The test statistic “means of phase A minus means of phase B” will be chosen as the primary outcome of this study. In case visual analysis suggests delayed effects, the randomization test will be repeated with lagged data until the lowest p-value has been reached (one effect lag equals one day). The first 4 measurements in the baseline phase will be removed before calculating the means, since we expect a learning effect on the first trials, leaving between 5 and 20 measurement points depending on the randomly decided length of the baseline period. Studies that tested the statistical properties of a randomization test used in this type of design showed that the Type I error probability of the randomization test was maintained at an acceptable level [ 61 ].

Missing data

Missing values from the repeated smartphone-based cognitive test will be treated with the randomized marker method [ 62 ]. In this method, the missing value is removed from calculation of the mean in the randomization test. In addition, the position of the missing value will be randomly reshuffled within possible randomization schemes of the study protocol. In this study, this means that a missing value in the baseline or beginning of the intervention phase may be reshuffled to be part of the other phase if this falls within the possibilities (i.e. if it falls within the first 4 to 8 weeks of the study). This method was found to be more effective at controlling type I error and resulted in higher power than multiple imputation and single imputation using a time series model in a SCED simulation study [ 63 ].

Secondary data

For the secondary study parameters (i.e. BDNF levels and results of the oral cognitive test battery) which are measured within a single-armed prospective design, statistical analysis will be done using IBM SPSS Statistics 27.

Secondary statistics

Before analysis, the data will be checked for outliers, defined as values lying further than three times the interquartile range away from the median value. The normality assumption will be checked based on visual representations of the data using histograms and measurements of skewness and kurtosis (normality assumed if the data values lie between -2 and + 2) [ 64 ]. Homoskedasticity will be tested using the Levene’s test. Descriptive statistics will be used. Furthermore, the Friedman test will be used for analysis of repeated measurements. The False Discovery Rate (FDR) method will be applied to correct for the multiple testing problem. In the FDR method, every p -value is compared against a sequentially weighted threshold on all p -values [ 65 ].

Missing pre-post values will not be replaced by specific values, but be excluded from statistical analysis.

Sample size calculation

The sample size needed for the abovementioned statistical analyses is different for the SCED and single-armed prospective study design. For the SCED design, the minimum sample size is n  = 1. Instead, the power of the analysis depends on the number of observations [ 40 , 41 ]. For randomization tests the lowest attainable p -value is calculated by dividing 1 by the number of possible permutations. In this study, the baseline phase, after removing the first four values because of a learning effect, can contain 5 to 24 measurement points, allowing 20 possible permutations. This corresponds with a lowest attainable p -value of 0.05 [ 56 ]. Of note, successful replication of the single-case experiment in additional participants with similar symptoms will improve the generalizability of the results [ 40 , 41 ].

For the single-armed prospective study design we have estimated the sample size needed in order to have sufficient power (Power = 0.80) for evaluating a Time effect (over 4 time points) of cognitive test performance changes with repeated measures ANOVA. We found no previous studies examining the effect of any muscle activity intervention in spinal cord injury subjects. Therefore, the required effect size was estimated to be similar to that from a meta-analysis examining the effect of resistance exercise interventions on general cognitive function in healthy adults [ 66 ]. The overall effect size (Cohen’s d) was 0.71 (0.30–1.12) for resistance exercise. The correlation among repeated measures for this cognitive test battery that was found to be ≥ 0.77 in a previous SCI study [ 43 ]. G*Power 3.1.9.7 estimated that the minimum total sample size should be 7. Taking into account potential drop-outs and the uncertainty of the effect, we decided to aim for a total sample of 15 included participants.

Data management

All the participant data will be coded and pseudomized to protect their privacy. The repeated cognitive test will be administered using a secured smartphone application (see www.m-path.io/landing ), which was designed for use in clinical settings and research and is General Data Protection Regulation (GDPR) compliant. Participants will register to m-Path with their study code instead of their personal name. With a password, one of the researchers can access a dashboard where data from all included participants will be visible. Other test results including participant identification details will be stored at Adelante Hoensbroek in a locked cabinet with restricted access. The key will be kept in another locked cabinet. The test results with participant identification will only be accessed again after coding and de-identifying the data in order to hand them over to participants requesting their test results. The data will be stored for 15 years. Blood samples will be destroyed after analysis, which is expected to take place within one year after blood collection. We intend to make pseudomized data from our study available according to the FAIR principle, such that it is Findable, Accessible, Interoperable and Reusable. Both negative and positive results will be made public. Our results will be presented on the Dutch national rehabilitation medicine congress and international rehabilitation/neuroscience congresses so that rehabilitation professionals are more aware of the benefit of muscle training or maintaining a healthy body overall on the brain and cognitive function, and so that neuroscientists will understand the relevance of neuromuscular electrical stimulation as one of the muscle training strategies to investigate the exercise-cognition link. The results will be conveyed locally to spinal cord injury subjects in our rehabilitation center in Hoensbroek and in the Maastricht University Medical Center (MUMC +). We will inform other national spinal cord injury clinics of the results and we will discuss the findings in the national work group of the Dutch rehabilitation society for movement and sport. Finally, the results will be submitted to a peer-reviewed journal.

Risks and harms

The use of electrical stimulation is generally safe. It has even been used unsupervised in spinal cord injury subjects during sleep [ 67 ]. However some adverse events or inconveniences that have been reported previously in literature are a red, raised or itchy skin; muscle pain; increased neuropathic pain; uncomfortable feeling; orthostatic hypotension (dizziness, light-headedness, blurred vision, palpitation or shortness of breath); in some cases pain from a spasm may occur. The adverse events reported are temporary added risks that disappear once the stimulus/stimulation has stopped [ 68 ]. In case the subject feels uncomfortable during electrical stimulation, they will be informed that the stimulation can be interrupted. Participants with a lesion level above T6 may experience autonomic dysreflexia in response to the electrical stimulation. Whenever this occurs, stopping the electrical stimulation should solve the problem. Participants with lesion levels above T6 will be explained how they can recognize signs of autonomic dysreflexia. Whenever autonomic dysreflexia has occurred during the NMES training period participants are asked to contact the medical professional who’s telephone number is provided to them at the beginning of the project and in the participant information. The sponsor has a liability insurance which covers for damage to research participants. All adverse events reported spontaneously by a participant or observed by the investigator will be recorded.

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Acknowledgements

This work was supported by University Fund Limburg/SWOL (reference CoBes 22.055) awarded to Wouter Vints. Johan W.S. Vlaeyen is supported by the ‘Asthenes’ long-term structural funding Methusalem grant by the Flemish Government, Belgium (METH/15/011) and an NOW Open Competition grant (nr. 406.20.GO.032). The sponsors were not involved in study design, collection, analysis or interpretation of the data, in writing the report, nor in the decision to submit the article for publication.

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Vints, W.A.J., Levin, O., van Griensven, M. et al. Neuromuscular electrical stimulation to combat cognitive aging in people with spinal cord injury: protocol for a single case experimental design study. BMC Neurol 24 , 197 (2024). https://doi.org/10.1186/s12883-024-03699-9

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• Spinal cord injury
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• Neuroplasticity
• Brain-derived neurotrophic factor
• Single case experimental design
• Chronic rehabilitation care

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Secretariat, if anyone remembers, won the triple crown at the Belmont Race Track on Long Island, located at the nexus of La Guardia, JFK Airports, the Long Island Railroad and multiple major highways. Belmont Race Track is now being rebuilt along with an adjacent UBS hockey arena for the New York Islanders which can be transmogrified into a...

Market by Met Council: Revolutionizing Food Pantries in the Digital Age

In fall 2023, the Food Program of Met Council—America’s largest Jewish charity dedicated to fighting poverty—completed the rollout of the newest version of its digital pantry platform to twelve food pantries in the Met Council food pantry network. The digital initiative coincided with a shift from food pantries’ traditional “pre-packed” model—in which pantry staff and volunteers pre-packed standardized bags of foods and handed them out to long lines of waiting clients (the standard model in the US)—to a “client choice” model, where clients could choose their own food items. Over half of the pantries in Met Council’s network were undergoing the transition to client choice. This case discusses the evolution of the digital pantry; specifically, the pros and cons of each pantry model from an operational efficiency perspective, how operational levers can influence consumers’ purchasing decisions, fairness in resource allocation problems, and “push” versus “pull” inventory distribution models.

In fall 2023, the Food Program of Met Council—America’s largest Jewish charity dedicated to fighting poverty—completed the rollout of the newest version of its digital pantry platform to twelve food pantries in the Met Council food pantry network. The digital initiative coincided with a shift from food pantries’ traditional “pre-packed” model—in...

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Investor Influence on Media Coverage: Evidence from Venture Capital-Backed Startups

We examine the role of investors on the media coverage of their private firm investments. Specifically, we survey VC investors and find that 78% of the respondents take active steps to increase their portfolio companies’ media coverage. The survey results also demonstrate that increased media coverage supports the companies with better recognition and branding and provides benefits to diverse stakeholders. We extend the survey results using empirical tests. Both active VC monitoring and VC reputation are related to stronger effects. Overall, our findings emphasize the role of investors in media coverage decisions of private firms.

We examine the role of investors on the media coverage of their private firm investments. Specifically, we survey VC investors and find that 78% of the respondents take active steps to increase their portfolio companies’ media coverage. The survey results also demonstrate that increased media coverage supports the companies with better recognition...

Business Experiments as Persuasion

Much of the prior work on experimentation rests upon the assumption that entrepreneurs and managers use—or should optimally adopt—a "scientific approach" to test possible decisions before making them. This paper offers an alternative view of experimental strategy, introducing the possibility that at least some business experiments privilege persuasion over generating unbiased information. In this view, actors may craft experiments designed to gain support for their ideas, even if doing so reduces the informativeness of the experiment. However, decision-makers are not naïve—they are aware that the results they are reviewing may be the product of a curated information environment. Using a formal model, this paper shows that under a wide range of conditions, actors prefer to enact a less than fully informative experiment designed to persuade—even when a fully informative experiment is feasible at the same cost.

Much of the prior work on experimentation rests upon the assumption that entrepreneurs and managers use—or should optimally adopt—a "scientific approach" to test possible decisions before making them. This paper offers an alternative view of experimental strategy, introducing the possibility that at least some business experiments privilege...

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The state of AI in early 2024: Gen AI adoption spikes and starts to generate value

If 2023 was the year the world discovered generative AI (gen AI) , 2024 is the year organizations truly began using—and deriving business value from—this new technology. In the latest McKinsey Global Survey  on AI, 65 percent of respondents report that their organizations are regularly using gen AI, nearly double the percentage from our previous survey just ten months ago. Respondents’ expectations for gen AI’s impact remain as high as they were last year , with three-quarters predicting that gen AI will lead to significant or disruptive change in their industries in the years ahead.

About the authors

This article is a collaborative effort by Alex Singla , Alexander Sukharevsky , Lareina Yee , and Michael Chui , with Bryce Hall , representing views from QuantumBlack, AI by McKinsey, and McKinsey Digital.

Organizations are already seeing material benefits from gen AI use, reporting both cost decreases and revenue jumps in the business units deploying the technology. The survey also provides insights into the kinds of risks presented by gen AI—most notably, inaccuracy—as well as the emerging practices of top performers to mitigate those challenges and capture value.

AI adoption surges

Interest in generative AI has also brightened the spotlight on a broader set of AI capabilities. For the past six years, AI adoption by respondents’ organizations has hovered at about 50 percent. This year, the survey finds that adoption has jumped to 72 percent (Exhibit 1). And the interest is truly global in scope. Our 2023 survey found that AI adoption did not reach 66 percent in any region; however, this year more than two-thirds of respondents in nearly every region say their organizations are using AI. 1 Organizations based in Central and South America are the exception, with 58 percent of respondents working for organizations based in Central and South America reporting AI adoption. Looking by industry, the biggest increase in adoption can be found in professional services. 2 Includes respondents working for organizations focused on human resources, legal services, management consulting, market research, R&D, tax preparation, and training.

Also, responses suggest that companies are now using AI in more parts of the business. Half of respondents say their organizations have adopted AI in two or more business functions, up from less than a third of respondents in 2023 (Exhibit 2).

Gen AI adoption is most common in the functions where it can create the most value

Most respondents now report that their organizations—and they as individuals—are using gen AI. Sixty-five percent of respondents say their organizations are regularly using gen AI in at least one business function, up from one-third last year. The average organization using gen AI is doing so in two functions, most often in marketing and sales and in product and service development—two functions in which previous research  determined that gen AI adoption could generate the most value 3 “ The economic potential of generative AI: The next productivity frontier ,” McKinsey, June 14, 2023. —as well as in IT (Exhibit 3). The biggest increase from 2023 is found in marketing and sales, where reported adoption has more than doubled. Yet across functions, only two use cases, both within marketing and sales, are reported by 15 percent or more of respondents.

Gen AI also is weaving its way into respondents’ personal lives. Compared with 2023, respondents are much more likely to be using gen AI at work and even more likely to be using gen AI both at work and in their personal lives (Exhibit 4). The survey finds upticks in gen AI use across all regions, with the largest increases in Asia–Pacific and Greater China. Respondents at the highest seniority levels, meanwhile, show larger jumps in the use of gen Al tools for work and outside of work compared with their midlevel-management peers. Looking at specific industries, respondents working in energy and materials and in professional services report the largest increase in gen AI use.

Investments in gen AI and analytical AI are beginning to create value

The latest survey also shows how different industries are budgeting for gen AI. Responses suggest that, in many industries, organizations are about equally as likely to be investing more than 5 percent of their digital budgets in gen AI as they are in nongenerative, analytical-AI solutions (Exhibit 5). Yet in most industries, larger shares of respondents report that their organizations spend more than 20 percent on analytical AI than on gen AI. Looking ahead, most respondents—67 percent—expect their organizations to invest more in AI over the next three years.

Where are those investments paying off? For the first time, our latest survey explored the value created by gen AI use by business function. The function in which the largest share of respondents report seeing cost decreases is human resources. Respondents most commonly report meaningful revenue increases (of more than 5 percent) in supply chain and inventory management (Exhibit 6). For analytical AI, respondents most often report seeing cost benefits in service operations—in line with what we found last year —as well as meaningful revenue increases from AI use in marketing and sales.

Inaccuracy: The most recognized and experienced risk of gen AI use

As businesses begin to see the benefits of gen AI, they’re also recognizing the diverse risks associated with the technology. These can range from data management risks such as data privacy, bias, or intellectual property (IP) infringement to model management risks, which tend to focus on inaccurate output or lack of explainability. A third big risk category is security and incorrect use.

Respondents to the latest survey are more likely than they were last year to say their organizations consider inaccuracy and IP infringement to be relevant to their use of gen AI, and about half continue to view cybersecurity as a risk (Exhibit 7).

Conversely, respondents are less likely than they were last year to say their organizations consider workforce and labor displacement to be relevant risks and are not increasing efforts to mitigate them.

In fact, inaccuracy— which can affect use cases across the gen AI value chain , ranging from customer journeys and summarization to coding and creative content—is the only risk that respondents are significantly more likely than last year to say their organizations are actively working to mitigate.

Some organizations have already experienced negative consequences from the use of gen AI, with 44 percent of respondents saying their organizations have experienced at least one consequence (Exhibit 8). Respondents most often report inaccuracy as a risk that has affected their organizations, followed by cybersecurity and explainability.

Our previous research has found that there are several elements of governance that can help in scaling gen AI use responsibly, yet few respondents report having these risk-related practices in place. 4 “ Implementing generative AI with speed and safety ,” McKinsey Quarterly , March 13, 2024. For example, just 18 percent say their organizations have an enterprise-wide council or board with the authority to make decisions involving responsible AI governance, and only one-third say gen AI risk awareness and risk mitigation controls are required skill sets for technical talent.

Bringing gen AI capabilities to bear

The latest survey also sought to understand how, and how quickly, organizations are deploying these new gen AI tools. We have found three archetypes for implementing gen AI solutions : takers use off-the-shelf, publicly available solutions; shapers customize those tools with proprietary data and systems; and makers develop their own foundation models from scratch. 5 “ Technology’s generational moment with generative AI: A CIO and CTO guide ,” McKinsey, July 11, 2023. Across most industries, the survey results suggest that organizations are finding off-the-shelf offerings applicable to their business needs—though many are pursuing opportunities to customize models or even develop their own (Exhibit 9). About half of reported gen AI uses within respondents’ business functions are utilizing off-the-shelf, publicly available models or tools, with little or no customization. Respondents in energy and materials, technology, and media and telecommunications are more likely to report significant customization or tuning of publicly available models or developing their own proprietary models to address specific business needs.

Respondents most often report that their organizations required one to four months from the start of a project to put gen AI into production, though the time it takes varies by business function (Exhibit 10). It also depends upon the approach for acquiring those capabilities. Not surprisingly, reported uses of highly customized or proprietary models are 1.5 times more likely than off-the-shelf, publicly available models to take five months or more to implement.

Gen AI high performers are excelling despite facing challenges

Gen AI is a new technology, and organizations are still early in the journey of pursuing its opportunities and scaling it across functions. So it’s little surprise that only a small subset of respondents (46 out of 876) report that a meaningful share of their organizations’ EBIT can be attributed to their deployment of gen AI. Still, these gen AI leaders are worth examining closely. These, after all, are the early movers, who already attribute more than 10 percent of their organizations’ EBIT to their use of gen AI. Forty-two percent of these high performers say more than 20 percent of their EBIT is attributable to their use of nongenerative, analytical AI, and they span industries and regions—though most are at organizations with less than $1 billion in annual revenue. The AI-related practices at these organizations can offer guidance to those looking to create value from gen AI adoption at their own organizations.

To start, gen AI high performers are using gen AI in more business functions—an average of three functions, while others average two. They, like other organizations, are most likely to use gen AI in marketing and sales and product or service development, but they’re much more likely than others to use gen AI solutions in risk, legal, and compliance; in strategy and corporate finance; and in supply chain and inventory management. They’re more than three times as likely as others to be using gen AI in activities ranging from processing of accounting documents and risk assessment to R&D testing and pricing and promotions. While, overall, about half of reported gen AI applications within business functions are utilizing publicly available models or tools, gen AI high performers are less likely to use those off-the-shelf options than to either implement significantly customized versions of those tools or to develop their own proprietary foundation models.

What else are these high performers doing differently? For one thing, they are paying more attention to gen-AI-related risks. Perhaps because they are further along on their journeys, they are more likely than others to say their organizations have experienced every negative consequence from gen AI we asked about, from cybersecurity and personal privacy to explainability and IP infringement. Given that, they are more likely than others to report that their organizations consider those risks, as well as regulatory compliance, environmental impacts, and political stability, to be relevant to their gen AI use, and they say they take steps to mitigate more risks than others do.

Gen AI high performers are also much more likely to say their organizations follow a set of risk-related best practices (Exhibit 11). For example, they are nearly twice as likely as others to involve the legal function and embed risk reviews early on in the development of gen AI solutions—that is, to “ shift left .” They’re also much more likely than others to employ a wide range of other best practices, from strategy-related practices to those related to scaling.

In addition to experiencing the risks of gen AI adoption, high performers have encountered other challenges that can serve as warnings to others (Exhibit 12). Seventy percent say they have experienced difficulties with data, including defining processes for data governance, developing the ability to quickly integrate data into AI models, and an insufficient amount of training data, highlighting the essential role that data play in capturing value. High performers are also more likely than others to report experiencing challenges with their operating models, such as implementing agile ways of working and effective sprint performance management.

About the research

The online survey was in the field from February 22 to March 5, 2024, and garnered responses from 1,363 participants representing the full range of regions, industries, company sizes, functional specialties, and tenures. Of those respondents, 981 said their organizations had adopted AI in at least one business function, and 878 said their organizations were regularly using gen AI in at least one function. To adjust for differences in response rates, the data are weighted by the contribution of each respondent’s nation to global GDP.

Alex Singla and Alexander Sukharevsky  are global coleaders of QuantumBlack, AI by McKinsey, and senior partners in McKinsey’s Chicago and London offices, respectively; Lareina Yee  is a senior partner in the Bay Area office, where Michael Chui , a McKinsey Global Institute partner, is a partner; and Bryce Hall  is an associate partner in the Washington, DC, office.

They wish to thank Kaitlin Noe, Larry Kanter, Mallika Jhamb, and Shinjini Srivastava for their contributions to this work.

This article was edited by Heather Hanselman, a senior editor in McKinsey’s Atlanta office.

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